Fast Kernel Classifiers with Online and Active Learning.
Dimension Reduction in Text Classification with Support Vector Machines.
Learning the Kernel Function via Regularization.
Concentration Bounds for Unigram Language Models.
Learning with Decision Lists of Data-Dependent Features.
Algorithmic Stability and Meta-Learning.
Assessing Approximate Inference for Binary Gaussian Process Classification.
Information Bottleneck for Gaussian Variables.
Frames, Reproducing Kernels, Regularization and Learning.
Multiclass Classification with Multi-Prototype Support Vector Machines.
Core Vector Machines: Fast SVM Training on Very Large Data Sets.
Tree-Based Batch Mode Reinforcement Learning.
A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data.
Universal Algorithms for Learning Theory Part I : Piecewise Constant Functions.
Characterization of a Family of Algorithms for Generalized Discriminant Analysis on Undersampled Problems.
Active Coevolutionary Learning of Deterministic Finite Automata.
Analysis of Variance of Cross-Validation Estimators of the Generalization Error.
Matrix Exponentiated Gradient Updates for On-line Learning and Bregman Projection.
Stability of Randomized Learning Algorithms.
Estimating Functions for Blind Separation When Sources Have Variance Dependencies.
Large Margin Methods for Structured and Interdependent Output Variables.
Efficient Margin Maximizing with Boosting.
Generalization Bounds for the Area Under the ROC Curve.
Combining Information Extraction Systems Using Voting and Stacked Generalization.
A Classification Framework for Anomaly Detection.
Semigroup Kernels on Measures.
Multiclass Boosting for Weak Classifiers.
Diffusion Kernels on Statistical Manifolds.
A Generalization Error for Q-Learning.
Separating a Real-Life Nonlinear Image Mixture.
Tutorial on Practical Prediction Theory for Classification.
Loopy Belief Propagation: Convergence and Effects of Message Errors.
Learning a Mahalanobis Metric from Equivalence Constraints.
Local Propagation in Conditional Gaussian Bayesian Networks.
Asymptotic Model Selection for Naive Bayesian Networks.
Quasi-Geodesic Neural Learning Algorithms Over the Orthogonal Group: A Tutorial.
Learning from Examples as an Inverse Problem.
Active Learning to Recognize Multiple Types of Plankton.
Asymptotics in Empirical Risk Minimization.
An MDP-Based Recommender System.
Estimation of Non-Normalized Statistical Models by Score Matching.
A Bayesian Model for Supervised Clustering with the Dirichlet Process Prior.
Kernel Methods for Measuring Independence.
Managing Diversity in Regression Ensembles.
Feature Selection for Unsupervised and Supervised Inference: The Emergence of Sparsity in a Weight-Based Approach.
On the Nyström Method for Approximating a Gram Matrix for Improved Kernel-Based Learning.
Expectation Consistent Approximate Inference.
Learning Hidden Variable Networks: The Information Bottleneck Approach.
Inner Product Spaces for Bayesian Networks.
A Unifying View of Sparse Approximate Gaussian Process Regression.
Generalization Bounds and Complexities Based on Sparsity and Clustering for Convex Combinations of Functions from Random Classes.
Adaptive Online Prediction by Following the Perturbed Leader.
Clustering on the Unit Hypersphere using von Mises-Fisher Distributions.
Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models.
Learning the Kernel with Hyperkernels.
Maximum Margin Algorithms with Boolean Kernels.
Convergence Theorems for Generalized Alternating Minimization Procedures.
A Modified Finite Newton Method for Fast Solution of Large Scale Linear SVMs.
New Horn Revision Algorithms.
Prioritization Methods for Accelerating MDP Solvers.
Efficient Computation of Gapped Substring Kernels on Large Alphabets.
Working Set Selection Using Second Order Information for Training Support Vector Machines.
Learning Module Networks.
A Bayes Optimal Approach for Partitioning the Values of Categorical Attributes.
Smooth epsiloon-Insensitive Regression by Loss Symmetrization.
Clustering with Bregman Divergences.
Variational Message Passing.
Learning Multiple Tasks with Kernel Methods.
Change Point Problems in Linear Dynamical Systems.
Gaussian Processes for Ordinal Regression.
What's Strange About Recent Events (WSARE): An Algorithm for the Early Detection of Disease Outbreaks.
Denoising Source Separation.
Machine Learning Methods for Predicting Failures in Hard Drives: A Multiple-Instance Application.
