ColorVein: Colorful Cancelable Vein Biometrics

Improving Fast Adversarial Training via Self-Knowledge Guidance

No Place to Hide: Dual Deep Interaction Channel Network for Fake News Detection With Data Augmentation

Curriculum-aware Training for Discriminating Molecular Property Prediction Models

EMOVA: Empowering language models to see, hear and speak with vivid emotions

Joint Gradient Balancing for Data Ordering in Finite-Sum Multi-Objective Optimization

A Survey on Time-Series Pre-Trained Models

Automated Dominative Subspace Mining for Efficient Neural Architecture Search

CFVNet: An End-to-End Cancelable Finger Vein Network for Recognition

Fooling the Image Dehazing Models by First Order Gradient

Illumination Controllable Dehazing Network based on Unsupervised Retinex Embedding

Mentored Learning: Improving Generalization and Convergence of Student Learner via Teaching Feedback

Power Law in Deep Neural Networks: Sparse Network Generation and Continual Learning With Preferential Attachment

Response Generation in Social Network with Topic and Emotion Constraints

Searching to Exploit Memorization Effect in Deep Learning With Noisy Labels

Forward-Backward Reasoning in Large Language Models for Mathematical Verification

Implicit Concept Removal of Diffusion Models

METAMATH: BOOTSTRAP YOUR OWN MATHEMATICAL QUESTIONS FOR LARGE LANGUAGE MODELS

MULTI-RESOLUTION DIFFUSION MODELS FOR TIME SERIES FORECASTING

PIXART-α: FAST TRAINING OF DIFFUSION TRANSFORMER FOR PHOTOREALISTIC TEXT-TO-IMAGE SYNTHESIS

AlignVE: Visual Entailment Recognition Based on Alignment Relations

Bilinear Scoring Function Search for Knowledge Graph Learning

Effective Structured Prompting by Meta-Learning and Representative Verbalizer

Feedback Pyramid Attention Networks for Single Image Super-Resolution

Learning the Relation Between Similarity Loss and Clustering Loss in Self-Supervised Learning

Non-autoregressive Conditional Diffusion Models for Time Series Prediction

Nonparametric Iterative Machine Teaching

Searching a High Performance Feature Extractor for Text Recognition Network

AN ADAPTIVE POLICY TO EMPLOY SHARPNESS-AWARE MINIMIZATION

Cross-Modal Matching and Adaptive Graph Attention Network for RGB-D Scene Recognition

ENHANCING META LEARNING VIA MULTI-OBJECTIVE SOFT IMPROVEMENT FUNCTIONS

GrowCLIP: Data-aware Automatic Model Growing for Large-scale Contrastive Language-Image Pre-training

KICGPT: Large Language Model with Knowledge in Context for Knowledge Graph Completion

Nonparametric Teaching for Multiple Learners

Positive-Unlabeled Node Classification with Structure-aware Graph Learning

TASK-CUSTOMIZED MASKED AUTOENCODER VIA MIXTURE OF CLUSTER-CONDITIONAL EXPERTS

Efficient Low-Rank Semidefinite Programming with Robust Loss Functions

Efficient low-rank tensor learning with nonconvex overlapped nuclear norm regularization

Efficient Variance Reduction for Meta-Learning

Low-rank Tensor Learning with Nonconvex Overlapped Nuclear Norm Regularization

New transformation method in continuous particle swarm optimisation for feature selection

Pyramidal dense attention networks for single image super-resolution

Subspace Learning for Effective Meta-Learning

Multi-Objective Deep Learning with Adaptive Reference Vectors

Query Rewriting in TaoBao Search

REVISITING OVER-SMOOTHING IN BERT FROM THE PERSPECTIVE OF GRAPH

Learning to Hash with Dimension Analysis Based Quantizer for Image Retrieval

Noniterative Sparse LS-SVM Based on Globally Representative Point Selection

Side Information Fusion for Recommender Systems over Heterogeneous Information Network

A scalable, adaptive and sound nonconvex regularizer for low-rank matrix learning

Dropout’s Dream Land: Generalization from Learned Simulators to Reality

Effective Meta-Regularization by Kernelized Proximal Regularization

SEEN: Few-Shot Classification with SElf-ENsemble

SparseBERT: Rethinking the Importance Analysis in Self-attention

Time Series Anomaly Detection with Multiresolution Ensemble Decoding

TOHAN: A One-step Approach towards Few-shot Hypothesis Adaptation

Bridging the gap between sample-based and one-shot neural architecture search with BONAS

Generalized Convolutional Sparse Coding with Unknown Noise

Timeseries anomaly detection using temporal hierarchical one-class network

Privacy-Preserving Stacking with Application to Cross-organizational Diabetes Prediction

Advances in Recommender Systems: From Multi-stakeholder Marketplaces to Automated RecSys

Effective decoding in graph auto-encoder using triadic closure

Efficient Neural Interaction Function Search for Collaborative Filtering

Policy Prediction Network: Model-Free Behavior Policy with Model-Based Learning in Continuous Action Space

Searching to exploit memorization effect in learning with noisy labels

Accelerated and Inexact Soft-Impute for Large-Scale Matrix and Tensor Completion

Communication-efficient distributed blockwise momentum sgd with error-feedback

Large-Scale Low-Rank Matrix Learning with Nonconvex Regularizers

Low-Rank Matrix Learning Using Biconvex Surrogate Minimization

Normalization helps training of quantized LSTM

Analysis of quantized models

Dynamic Unit Surgery for Deep Neural Network Compression and Acceleration

Efficient nonconvex regularized tensor completion with structure-aware proximal iterations

Efficient nonconvex regularized tensor completion with structure-aware proximal iterations

Privacy-preserving stacking with application to cross-organizational diabetes prediction

Corrigendum to “Multi-label learning in the independent label sub-spaces” [Pattern Recognition Letters 97(2017) 8–12] (S0167865517302295) (10.1016/j.patrec.2017.06.024))

Efficient learning with a family of nonconvex regularizers by redistributing nonconvexity

Fast-Solving Quasi-Optimal LS-S³VM Based on an Extended Candidate Set

Multi-label learning with global and local label correlation

Scalable online convolutional sparse coding

Scalable robust matrix factorization with nonconvex loss

Lightweight stochastic optimization for minimizing finite sums with infinite data

Lightweight Stochastic Optimization for Minimizing Finite Sums with Infinite Data

Loss-aware weight quantization of deep networks

Online Convolutional sparse coding with sample-dependent dictionary

Online Convolutional Sparse Coding with Sample-Dependent Dictionary

A Note on the Unification of Adaptive Online Learning

Multi-Label learning in the independent label sub-spaces

Collaborative filtering with social local models

Efficient inexact proximal gradient algorithm for nonconvex problems

Efficient sparse low-rank tensor completion using the frank-wolfe algorithm

Follow the moving leader in deep learning

Follow the Moving Leader in Deep Learning

Loss-aware binarization of deep networks

Training neural-network-based controller on distributed machine learning platform for power electronics systems

Zero-shot learning with a partial set of observed attributes

Fast-and-light stochastic ADMM

Greedy learning of generalized low-rank models

Special issue: First International Conference on Big Data and Smart Computing (BigComp2014)

Aggregating crowdsourced ordinal labels via bayesian clustering

Asynchronous distributed semi-stochastic gradient optimization

Efficient learning of timeseries shapelets

Efficient Learning with a Family of Nonconvex Regularizers by Redistributing Nonconvexity

Efficient learning with a family of nonconvex regularizes by redistributing nonconvexity

Fast low-rank matrix learning with nonconvex regularization

Fast nonsmooth regularized risk minimization with continuation

Towards safe semi-supervised learning for multivariate performance measures

Bayes-Optimal Hierarchical Multilabel Classification

Fast second-order stochastic backpropagation for variational inference

Large-scale nyström kernel matrix approximation using randomized SVD

Scalable Nonparametric Low-Rank Kernel Learning Using Block Coordinate Descent

Scaling up graph-based semisupervised learning via prototype vector machines

Welcome from IEEE DSAA'2015 chairs

Machine Learning

Accelerated inexact soft-impute for fast large-scale matrix completion

Collaborative filtering via co-factorization of individuals and groups

Colorization by patch-based local low-rank matrix completion

Accelerated stochastic gradient method for composite regularization

Mandatory leaf node prediction in hierarchical multilabel classification

Selected papers from the 2011 International Conference on Neural Information Processing (ICONIP 2011)

Simple randomized algorithms for online learning with kernels

Accurate integration of aerosol predictions by smoothing on a manifold

Asynchronous distributed ADMM for consensus optimization

Asynchronous Distributed ADMM for Consensus Optimization

Fast stochastic alternating direction method of multipliers

Fast Stochastic Alternating Direction Method of Multipliers

Gradient descent with proximal average for nonconvex and composite regularization

Learning to predict from crowd sourced data

Learning to Predict from Crowdsourced Data

Multilabel classification with label correlations and missing labels

Convex and scalable weakly labeled SVMs

Efficient learning for models with DAG-structured parameter constraints

Accurate probability calibration for multiple classifiers

Covariate shift in Hilbert space: A solution via surrogate kernels

Efficient kernel learning from side information using ADMM

Efficient multi-label classification with many labels

Flexible nonparametric kernel learning with different loss functions

Learning from High-Dimensional Data in Multitask/Multilabel Classification

A brief introduction to the special issue for ISNN2010

Bilinear probabilistic principal component analysis

Efficient sparse modeling with automatic feature grouping

Convex multitask learning with flexible task clusters

Hierarchical multilabel classification with minimum Bayes risk

Mandatory leaf node prediction in hierarchical multilabel classification

A hybrid PSO-BFGS strategy for global optimization of multimodal functions

Domain adaptation via transfer component analysis

Incorporating cellular sorting structure for better prediction of protein subcellular locations

Neural Information Processing - 18th International Conference, Proceedings

Efficient sparse modeling with automatic feature grouping

Multi-label classification on tree- and DAG-structured hierarchies

Structured clustering with automatic kernel adaptation

Time and space efficient spectral clustering via column sampling

Clustered Nyström method for large scale manifold learning and dimension reduction

Fast and accurate kernel density approximation using a divide-and-conquer approach

Incorporating the loss function into discriminative clustering of structured outputs

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface

Simplifying mixture models through function approximation

Text detection in images using sparse representation with discriminative dictionaries

Advances in Neural Networks--ISNN 2010: 7th International Symposium on Neural Networks, ISNN, Shanghai, China, June 6-9, 2010: proceedings

Cost-sensitive semi-supervised support vector machine

Making large-scale Nyström approximation possible

Manifold regularization for structured outputs via the joint kernel

Online multiple instance learning with no regret

Spectral and semidefinite relaxations of the CLUHSIC algorithm

Building sparse multiple-kernel SVM classifiers

Combo-dimensional kernels for graph classification

Density-Weighted nyström method for computing large kernel eigen-systems

Maximum margin clustering made practical

Maximum penalized likelihood Kernel regression for fast adaptation

A convex method for locating regions of interest with multi-instance learning

Accelerated gradient method for multi-task sparse learning problem

Accelerated gradient methods for stochastic optimization and online learning

Domain adaptation via transfer component analysis

Maximum margin clustering with multivariate loss function

Multiple kernel clustering

Prototype vector machine for large scale semi-supervised learning

Semi-Supervised learning using label mean

Tighter and convex maximum margin clustering

Unsupervised maximum margin feature selection with manifold regularization

Large-scale maximum margin discriminant analysis using core vector machines

Matrix-variate factor analysis and its applications

Sliced coordinate analysis for effective dimension reduction and nonlinear extensions

Improved Nyström low-rank approximation and error analysis

Transfer learning via dimensionality reduction

Transferring localization models across space

A class of single-class minimax probability machines for novelty detection

End-to-end privacy control in service outsourcing of human intensive processes: A multi-layered Web service integration approach

Ensembles of partially trained SVMs with multiplicative updates

Face recognition using spectral features

Marginalized multi-instance kernels

Surrogate maximization/minimization algorithms and extensions

SVDD-based pattern denoising

Adaptive localization in a dynamic WiFi environment through multi-view learning

Large-scale sparsified manifold regularization

Maximum margin clustering made practical

Simpler core vector machines with enclosing balls

Simplifying mixture models through function approximation

Surrogate maximization/minimization algorithms and extensions

A novel incremental principal component analysis and its application for face recognition

Efficient hyperkernel learning using second-order cone programming

Embedded kernel eigenvoice speaker adaptation and its implication to reference speaker weighting

Generalized core vector machines

Model-based transductive learning of the kernel matrix

Multidimensional vector regression for accurate and low-cost location estimation in pervasive computing

Structural, Syntactic and Statistical Pattern Recognition: Joint IAPR International Workshops, SSPR 2006 and SPR 2006, Hong Kong, China, August 17-19, 2006: proceedings

A regularization framework for multiple-instance learning

Accelerated convergence using dynamic mean shift

Block-quantized kernel matrix for fast spectral embedding

Diversified SVM ensembles for large data sets

Efficient classification of multi-label and imbalanced data using min-max modular classifiers

Efficient kernel feature extraction for massive data sets

Facial image reconstruction by SVDD-based pattern de-noising

Fast speaker adaption via maximum penalized likelihood kernel regression

Gene feature extraction using T-test statistics and kernel partial least squares

Learning the kernel in mahalanobis one-class support vector machines

Locally adaptive classification piloted by uncertainty

Multimodal registration using the discrete wavelet frame transform

Wavelet-based feature extraction for microarray data classification

Accurate and low-cost location estimation using kernels

Core vector machines: Fast SVM training on very large data sets

Kernel eigenvoice speaker adaptation

Applying neighborhood consistency for fast clustering and kernel density estimation

Core vector regression for very large regression problems

Data-dependent kernels for high-dimensional data classification

Kernel relevant component analysis for distance metric learning

Page segmentation using mathematical morphology

Pattern de-noising based on support ector data description

Position estimation tor wireless sensor networks

Towards end-to-end privacy control in the outsourcing of marketing activities: A web service integration solution

Very large SVM training using Core Vector Machines

A study of various composite kernels for kernel eigenvoice speaker adaptation

Dissimilarity learning for nominal data

Efficient hyperkernel learning using second-order cone programming

Fusing images with different focuses using support vector machines

Scaling up support vector data description by using core-sets

Signal de-noising by improving soft thresholding on the dyadic wavelet transform

The pre-image problem in kernel methods

Bayesian inference for transductive learning of kernel matrix using the tanner-wong data augmentation algorithm

Bayesian inference on principal component analysis using reversible jump markov chain Monte Carlo

Eigenvoice speaker adaptation via composite kernel PCA

Incremental PCA based face recognition

Speedup of Kernel Eigenvoice Speaker Adaptation by Embedded Kernel PCA

Surrogate maximization/minimization algorithms for AdaBoost and the logistic regression model

Text extraction using edge detection and morphological dilation

Using kernel PCA to improve eigenvoice speaker adaptation

Linear dependency between ε and the input noise in ε-support vector regression

Mining customer product ratings for personalized marketing

Parametric distance metric learning with label information

Texture classification using the support vector machines

Distance metric learning with kernels

Incremental eigen decomposition

Learning with Idealized Kernels

The Pre-Image Problem in Kernel Methods

Improving de-noising by coefficient de-noising and dyadic wavelet transform

Multifocus image fusion using artificial neural networks

Using the discrete wavelet frame transform to merge Landsat TM and SPOT panchromatic images

Finding the pre-images in kernel principal component analysis

Fusing images with multiple focuses using support vector machines

Fusing images with multiple focuses using support vector machines

Combination of images with diverse focuses using the spatial frequency

Linear dependency between epsilon and the input noise in epsilon-support vector regression

Applying the Bayesian evidence framework to nu-support vector regression

Applying the Bayesian evidence framework to ν-support vector regression

Bayesian support vector regression

Linear dependency between ϵ and the input noise in ϵ-support vector regression

Mining customer preference ratings for product recommendation using the support vector machine and the latent class model

Rival penalized competitive learning for model-based sequence clustering

The evidence framework applied to support vector machines

An extended genetic rule induction algorithm

Moderating the outputs of support vector machine classifiers

Integrating the evidence framework and the support vector machine

Moderating the outputs of support vector machine classifiers

Automated text categorization using support vector machine

Support vector mixture for classification and regression problems

Constructive algorithms for structure learning in feedforward neural networks for regression problems

Objective functions for training new hidden units in constructive neural networks

Use of bias term in projection pursuit learning improves approximation and convergence properties

Bayesian regularization in constructive neural networks

Reference priors for neural networks: Laplace versus Gaussian

Improving the approximation and convergence capabilities of projection pursuit learning

Efficient cross-validation for feedforward neural networks

A theoretically sound learning algorithm for constructive neural networks

Constructive neural networks: some practical considerations

Experimental analysis of input weight freezing in constructive neural networks

Accelerated and Inexact Soft-Impute for Large-Scale Matrix and Tensor Completion

Article

Communication-efficient distributed blockwise momentum sgd with error-feedback

Article

Large-Scale Low-Rank Matrix Learning with Nonconvex Regularizers

Article

Low-Rank Matrix Learning Using Biconvex Surrogate Minimization

Article

Normalization helps training of quantized LSTM

Article

Analysis of quantized models

Conference paper

Dynamic Unit Surgery for Deep Neural Network Compression and Acceleration

Conference paper

Efficient nonconvex regularized tensor completion with structure-aware proximal iterations

Conference paper

Efficient nonconvex regularized tensor completion with structure-aware proximal iterations

Conference paper

Privacy-preserving stacking with application to cross-organizational diabetes prediction

Conference paper

Corrigendum to “Multi-label learning in the independent label sub-spaces” [Pattern Recognition Letters 97(2017) 8–12] (S0167865517302295) (10.1016/j.patrec.2017.06.024))

Article

Efficient learning with a family of nonconvex regularizers by redistributing nonconvexity

Article

Fast-Solving Quasi-Optimal LS-S³VM Based on an Extended Candidate Set

Article

Multi-label learning with global and local label correlation

Article

Scalable online convolutional sparse coding

Article

Scalable robust matrix factorization with nonconvex loss

Article

Lightweight stochastic optimization for minimizing finite sums with infinite data

Conference paper

Lightweight Stochastic Optimization for Minimizing Finite Sums with Infinite Data

Conference paper

Loss-aware weight quantization of deep networks

Conference paper

Online Convolutional sparse coding with sample-dependent dictionary

Conference paper

Online Convolutional Sparse Coding with Sample-Dependent Dictionary

Conference paper

A Note on the Unification of Adaptive Online Learning

Article

Multi-Label learning in the independent label sub-spaces

Article

Collaborative filtering with social local models

Conference paper

Efficient inexact proximal gradient algorithm for nonconvex problems

Conference paper

Efficient sparse low-rank tensor completion using the frank-wolfe algorithm

Conference paper

Follow the moving leader in deep learning

Conference paper

Follow the Moving Leader in Deep Learning

Conference paper

Loss-aware binarization of deep networks

Conference paper

Training neural-network-based controller on distributed machine learning platform for power electronics systems

Conference paper

Zero-shot learning with a partial set of observed attributes

Conference paper

Fast-and-light stochastic ADMM

Article

Greedy learning of generalized low-rank models

Article

Special issue: First International Conference on Big Data and Smart Computing (BigComp2014)

Article

Aggregating crowdsourced ordinal labels via bayesian clustering

Conference paper

Asynchronous distributed semi-stochastic gradient optimization

Conference paper

Efficient learning of timeseries shapelets

Conference paper

Efficient Learning with a Family of Nonconvex Regularizers by Redistributing Nonconvexity

Conference paper

Efficient learning with a family of nonconvex regularizes by redistributing nonconvexity

Conference paper

Fast low-rank matrix learning with nonconvex regularization

Conference paper

Fast nonsmooth regularized risk minimization with continuation

Conference paper

Towards safe semi-supervised learning for multivariate performance measures

Conference paper

Bayes-Optimal Hierarchical Multilabel Classification

Article

Fast second-order stochastic backpropagation for variational inference

Article

Large-scale nyström kernel matrix approximation using randomized SVD

Article

Scalable Nonparametric Low-Rank Kernel Learning Using Block Coordinate Descent

Article

Scaling up graph-based semisupervised learning via prototype vector machines

Article

Welcome from IEEE DSAA'2015 chairs

Article

Machine Learning

Book chapter

Accelerated inexact soft-impute for fast large-scale matrix completion

Conference paper

Collaborative filtering via co-factorization of individuals and groups

Conference paper

Colorization by patch-based local low-rank matrix completion

Conference paper

Accelerated stochastic gradient method for composite regularization

Article

Mandatory leaf node prediction in hierarchical multilabel classification

Article

Selected papers from the 2011 International Conference on Neural Information Processing (ICONIP 2011)

Article

Simple randomized algorithms for online learning with kernels

Article

Accurate integration of aerosol predictions by smoothing on a manifold

Conference paper

Asynchronous distributed ADMM for consensus optimization

Conference paper

Asynchronous Distributed ADMM for Consensus Optimization

Conference paper

Fast stochastic alternating direction method of multipliers

Conference paper

Fast Stochastic Alternating Direction Method of Multipliers

Conference paper

Gradient descent with proximal average for nonconvex and composite regularization

Conference paper

Learning to predict from crowd sourced data

Conference paper

Learning to Predict from Crowdsourced Data

Conference paper

Multilabel classification with label correlations and missing labels

Conference paper

Convex and scalable weakly labeled SVMs

Article

Efficient learning for models with DAG-structured parameter constraints

Article

Accurate probability calibration for multiple classifiers

Conference paper

Covariate shift in Hilbert space: A solution via surrogate kernels

Conference paper

Efficient kernel learning from side information using ADMM

Conference paper

Efficient multi-label classification with many labels

Conference paper

Flexible nonparametric kernel learning with different loss functions

Conference paper

Learning from High-Dimensional Data in Multitask/Multilabel Classification

Conference paper

A brief introduction to the special issue for ISNN2010

Article

Bilinear probabilistic principal component analysis

Article

Efficient sparse modeling with automatic feature grouping

Article

Convex multitask learning with flexible task clusters

Conference paper

Hierarchical multilabel classification with minimum Bayes risk

Conference paper

Mandatory leaf node prediction in hierarchical multilabel classification

Conference paper

A hybrid PSO-BFGS strategy for global optimization of multimodal functions

Article

Domain adaptation via transfer component analysis

Article

Incorporating cellular sorting structure for better prediction of protein subcellular locations

Article

Neural Information Processing - 18th International Conference, Proceedings

Book

Efficient sparse modeling with automatic feature grouping

Conference paper

Multi-label classification on tree- and DAG-structured hierarchies

Conference paper

Structured clustering with automatic kernel adaptation

Conference paper

Time and space efficient spectral clustering via column sampling

Conference paper

Clustered Nyström method for large scale manifold learning and dimension reduction

Article

Fast and accurate kernel density approximation using a divide-and-conquer approach

Article

Incorporating the loss function into discriminative clustering of structured outputs

Article

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface

Article

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface

Article

Simplifying mixture models through function approximation

Article

Text detection in images using sparse representation with discriminative dictionaries

Article

Advances in Neural Networks--ISNN 2010: 7th International Symposium on Neural Networks, ISNN, Shanghai, China, June 6-9, 2010: proceedings

Book

Cost-sensitive semi-supervised support vector machine

Conference paper

Making large-scale Nyström approximation possible

Conference paper

Manifold regularization for structured outputs via the joint kernel

Conference paper

Online multiple instance learning with no regret

Conference paper

Spectral and semidefinite relaxations of the CLUHSIC algorithm

Conference paper

Building sparse multiple-kernel SVM classifiers

Article

Combo-dimensional kernels for graph classification

Article

Density-Weighted nyström method for computing large kernel eigen-systems

Article

Maximum margin clustering made practical

Article

Maximum penalized likelihood Kernel regression for fast adaptation

Article

A convex method for locating regions of interest with multi-instance learning

Conference paper

Accelerated gradient method for multi-task sparse learning problem

Conference paper

Accelerated gradient methods for stochastic optimization and online learning

Conference paper

Domain adaptation via transfer component analysis

Conference paper

Maximum margin clustering with multivariate loss function

Conference paper

Multiple kernel clustering

Conference paper

Prototype vector machine for large scale semi-supervised learning

Conference paper

Semi-Supervised learning using label mean

Conference paper

Tighter and convex maximum margin clustering

Conference paper

Unsupervised maximum margin feature selection with manifold regularization

Conference paper

Large-scale maximum margin discriminant analysis using core vector machines

Article

Matrix-variate factor analysis and its applications

Article

Sliced coordinate analysis for effective dimension reduction and nonlinear extensions

Article

Improved Nyström low-rank approximation and error analysis

Conference paper

Transfer learning via dimensionality reduction

Conference paper

Transferring localization models across space

Conference paper

A class of single-class minimax probability machines for novelty detection

Article

End-to-end privacy control in service outsourcing of human intensive processes: A multi-layered Web service integration approach

Article

Ensembles of partially trained SVMs with multiplicative updates

Article

Face recognition using spectral features

Article

Marginalized multi-instance kernels

Article

Surrogate maximization/minimization algorithms and extensions

Article

SVDD-based pattern denoising

Article

Adaptive localization in a dynamic WiFi environment through multi-view learning

Conference paper

Large-scale sparsified manifold regularization

Conference paper

Maximum margin clustering made practical

Conference paper

Simpler core vector machines with enclosing balls

Conference paper

Simplifying mixture models through function approximation

Conference paper

Surrogate maximization/minimization algorithms and extensions

Conference paper

A novel incremental principal component analysis and its application for face recognition

Article

Efficient hyperkernel learning using second-order cone programming

Article

Embedded kernel eigenvoice speaker adaptation and its implication to reference speaker weighting

Article

Generalized core vector machines

Article

Model-based transductive learning of the kernel matrix

Article

Multidimensional vector regression for accurate and low-cost location estimation in pervasive computing

Article

Structural, Syntactic and Statistical Pattern Recognition: Joint IAPR International Workshops, SSPR 2006 and SPR 2006, Hong Kong, China, August 17-19, 2006: proceedings

Book

A regularization framework for multiple-instance learning

Conference paper

Accelerated convergence using dynamic mean shift

Conference paper

Block-quantized kernel matrix for fast spectral embedding

Conference paper

Diversified SVM ensembles for large data sets

Conference paper

Efficient classification of multi-label and imbalanced data using min-max modular classifiers

Conference paper

Efficient kernel feature extraction for massive data sets

Conference paper

Facial image reconstruction by SVDD-based pattern de-noising

Conference paper

Fast speaker adaption via maximum penalized likelihood kernel regression

Conference paper

Gene feature extraction using T-test statistics and kernel partial least squares

Conference paper

Learning the kernel in mahalanobis one-class support vector machines

Conference paper

Locally adaptive classification piloted by uncertainty

Conference paper

Multimodal registration using the discrete wavelet frame transform

Conference paper

Wavelet-based feature extraction for microarray data classification

Conference paper

Accurate and low-cost location estimation using kernels

Article

Core vector machines: Fast SVM training on very large data sets

Article

Kernel eigenvoice speaker adaptation

Article

Applying neighborhood consistency for fast clustering and kernel density estimation

Conference paper

Core vector regression for very large regression problems

Conference paper

Data-dependent kernels for high-dimensional data classification

Conference paper

Kernel relevant component analysis for distance metric learning

Conference paper

Page segmentation using mathematical morphology

Conference paper

Pattern de-noising based on support ector data description

Conference paper

Position estimation tor wireless sensor networks

Conference paper

Towards end-to-end privacy control in the outsourcing of marketing activities: A web service integration solution

Conference paper

Very large SVM training using Core Vector Machines

Conference paper

A study of various composite kernels for kernel eigenvoice speaker adaptation

Article

Dissimilarity learning for nominal data

Article

Efficient hyperkernel learning using second-order cone programming

Article

Fusing images with different focuses using support vector machines

Article

Scaling up support vector data description by using core-sets

Article

Signal de-noising by improving soft thresholding on the dyadic wavelet transform

Article

The pre-image problem in kernel methods

Article

Bayesian inference for transductive learning of kernel matrix using the tanner-wong data augmentation algorithm

Conference paper

Bayesian inference on principal component analysis using reversible jump markov chain Monte Carlo

Conference paper

Eigenvoice speaker adaptation via composite kernel PCA

Conference paper

Incremental PCA based face recognition

Conference paper

Speedup of Kernel Eigenvoice Speaker Adaptation by Embedded Kernel PCA

Conference paper

Surrogate maximization/minimization algorithms for AdaBoost and the logistic regression model

Conference paper

Text extraction using edge detection and morphological dilation

Conference paper

Using kernel PCA to improve eigenvoice speaker adaptation

Conference paper

Linear dependency between ε and the input noise in ε-support vector regression

Article

Mining customer product ratings for personalized marketing

Article

Parametric distance metric learning with label information

Article

Texture classification using the support vector machines

Article

Distance metric learning with kernels

Conference paper

Incremental eigen decomposition

Conference paper

Learning with Idealized Kernels

Conference paper

The Pre-Image Problem in Kernel Methods

Conference paper

Improving de-noising by coefficient de-noising and dyadic wavelet transform

Article

Multifocus image fusion using artificial neural networks

Article

Using the discrete wavelet frame transform to merge Landsat TM and SPOT panchromatic images

Article

Finding the pre-images in kernel principal component analysis

Conference paper

Fusing images with multiple focuses using support vector machines

Conference paper

Fusing images with multiple focuses using support vector machines

Conference paper

Combination of images with diverse focuses using the spatial frequency

Article

Linear dependency between epsilon and the input noise in epsilon-support vector regression

Article

Applying the Bayesian evidence framework to nu-support vector regression

Conference paper

Applying the Bayesian evidence framework to ν-support vector regression

Conference paper

Bayesian support vector regression

Conference paper

Linear dependency between ϵ and the input noise in ϵ-support vector regression

Conference paper

Mining customer preference ratings for product recommendation using the support vector machine and the latent class model

Article

Rival penalized competitive learning for model-based sequence clustering

Article

The evidence framework applied to support vector machines

Article

An extended genetic rule induction algorithm

Conference paper

Moderating the outputs of support vector machine classifiers

Article

Integrating the evidence framework and the support vector machine

Conference paper

Moderating the outputs of support vector machine classifiers

Conference paper

Automated text categorization using support vector machine

Conference paper

Support vector mixture for classification and regression problems

Conference paper

Constructive algorithms for structure learning in feedforward neural networks for regression problems

Article

Objective functions for training new hidden units in constructive neural networks

Article

Use of bias term in projection pursuit learning improves approximation and convergence properties

Article

Bayesian regularization in constructive neural networks

Conference paper

Reference priors for neural networks: Laplace versus Gaussian

Conference paper

Improving the approximation and convergence capabilities of projection pursuit learning

Article

Efficient cross-validation for feedforward neural networks

Conference paper

A theoretically sound learning algorithm for constructive neural networks

Conference paper

Constructive neural networks: some practical considerations

Conference paper

Experimental analysis of input weight freezing in constructive neural networks

Conference paper