The Need for Open Source Software in Machine Learning.
Dynamics and Generalization Ability of LVQ Algorithms.
Combining PAC-Bayesian and Generic Chaining Bounds.
Multi-Task Learning for Classification with Dirichlet Process Priors.
Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts.
Nonlinear Estimators and Tail Bounds for Dimension Reduction in
Hierarchical Average Reward Reinforcement Learning.
Boosted Classification Trees and Class Probability/Quantile Estimation.
A Generalized Maximum Entropy Approach to Bregman Co-clustering and Matrix Approximation.
Harnessing the Expertise of 70, 000 Human Editors: Knowledge-Based Feature Generation for Text Categorization.
Learning in Environments with Unknown Dynamics: Towards more Robust Concept Learners.
Graph Laplacians and their Convergence on Random Neighborhood Graphs.
Transfer Learning via Inter-Task Mappings for Temporal Difference Learning.
Structure and Majority Classes in Decision Tree Learning.
Preventing Over-Fitting during Model Selection via Bayesian Regularisation of the Hyper-Parameters.
Stagewise Lasso.
Bayesian Quadratic Discriminant Analysis.
Integrating Naïve Bayes and FOIL.
Covariate Shift Adaptation by Importance Weighted Cross Validation.
Learning Equivariant Functions with Matrix Valued Kernels.
"Ideal Parent" Structure Learning for Continuous Variable Bayesian Networks.
Compression-Based Averaging of Selective Naive Bayes Classifiers.
AdaBoost is Consistent.
Undercomplete Blind Subspace Deconvolution.
Truncating the Loop Series Expansion for Belief Propagation.
Sparseness vs Estimating Conditional Probabilities: Some Asymptotic Results.
Behavioral Shaping for Geometric Concepts.
Spherical-Homoscedastic Distributions: The Equivalency of Spherical and Normal Distributions in Classification.
Statistical Consistency of Kernel Canonical Correlation Analysis.
Consistent Feature Selection for Pattern Recognition in Polynomial Time.
Proto-value Functions: A Laplacian Framework for Learning Representation and Control in Markov Decision Processes.
Gini Support Vector Machine: Quadratic Entropy Based Robust Multi-Class Probability Regression.
Multi-class Protein Classification Using Adaptive Codes.
Anytime Learning of Decision Trees.
Fast Iterative Kernel Principal Component Analysis.
Relational Dependency Networks.
Polynomial Identification in the Limit of Substitutable Context-free Languages.
A Unified Continuous Optimization Framework for Center-Based Clustering Methods.
Nonlinear Boosting Projections for Ensemble Construction.
Value Regularization and Fenchel Duality.
Learning Horn Expressions with LOGAN-H.
Bilinear Discriminant Component Analysis.
The Locally Weighted Bag of Words Framework for Document Representation.
Margin Trees for High-dimensional Classification.
Noise Tolerant Variants of the Perceptron Algorithm.
Characterizing the Function Space for Bayesian Kernel Models.
Distances between Data Sets Based on Summary Statistics.
From External to Internal Regret.
A Nonparametric Statistical Approach to Clustering via Mode Identification.
Measuring Differentiability: Unmasking Pseudonymous Authors.
Estimating High-Dimensional Directed Acyclic Graphs with the PC-Algorithm.
Local Discriminant Wavelet Packet Coordinates for Face Recognition.
An Interior-Point Method for Large-Scale
On the Consistency of Multiclass Classification Methods.
Dynamic Conditional Random Fields: Factorized Probabilistic Models for Labeling and Segmenting Sequence Data.
Classification in Networked Data: A Toolkit and a Univariate Case Study.
Learning to Classify Ordinal Data: The Data Replication Method.
Infinitely Imbalanced Logistic Regression.
Ranking the Best Instances.
A Probabilistic Analysis of EM for Mixtures of Separated, Spherical Gaussians.
On the Effectiveness of Laplacian Normalization for Graph Semi-supervised Learning.
Separating Models of Learning from Correlated and Uncorrelated Data.
Refinable Kernels.
The Pyramid Match Kernel: Efficient Learning with Sets of Features.
VC Theory of Large Margin Multi-Category Classifiers.
PAC-Bayes Risk Bounds for Stochastic Averages and Majority Votes of Sample-Compressed Classifiers.
Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis.
Attribute-Efficient and Non-adaptive Learning of Parities and DNF Expressions.
Learnability of Gaussians with Flexible Variances.
Concave Learners for Rankboost.
Online Learning of Multiple Tasks with a Shared Loss.
Handling Missing Values when Applying Classification Models.
General Polynomial Time Decomposition Algorithms.
Synergistic Face Detection and Pose Estimation with Energy-Based Models.
Building Blocks for Variational Bayesian Learning of Latent Variable Models.
A Complete Characterization of a Family of Solutions to a Generalized Fisher Criterion.
Very Fast Online Learning of Highly Non Linear Problems.
A Stochastic Algorithm for Feature Selection in Pattern Recognition.
Comments on the "Core Vector Machines: Fast SVM Training on Very Large Data Sets".
Maximum Entropy Density Estimation with Generalized Regularization and an Application to Species Distribution Modeling.
A New Probabilistic Approach in Rank Regression with Optimal Bayesian Partitioning.
Generalization Error Bounds in Semi-supervised Classification Under the Cluster Assumption.
Large Margin Semi-supervised Learning.
The On-Line Shortest Path Problem Under Partial Monitoring.
Minimax Regret Classifier for Imprecise Class Distributions.
Revised Loss Bounds for the Set Covering Machine and Sample-Compression Loss Bounds for Imbalanced Data.
Loop Corrections for Approximate Inference on Factor Graphs.
On the Representer Theorem and Equivalent Degrees of Freedom of SVR.
Unlabeled Compression Schemes for Maximum Classes.
Penalized Model-Based Clustering with Application to Variable Selection.
Euclidean Embedding of Co-occurrence Data.
