NDLI: Online learning of conditionally I.I.D. data

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Apprenticeship learning via inverse reinforcement learning

Learning to track 3D human motion from silhouettes

A multiplicative up-propagation algorithm

Gaussian process classification for segmenting and annotating sequences

Redundant feature elimination for multi-class problems

Multiple kernel learning, conic duality, and the SMO algorithm

Feature subset selection for learning preferences: a case study

An information theoretic analysis of maximum likelihood mixture estimation for exponential families

Unifying collaborative and content-based filtering

C4.5 competence map: a phase transition-inspired approach

Integrating constraints and metric learning in semi-supervised clustering

Variational methods for the Dirichlet process

Semi-supervised learning using randomized mincuts

Nonparametric classification with polynomial MPMC cascades

Estimating replicability of classifier learning experiments

Co-EM support vector learning

Active learning of label ranking functions

Ensemble selection from libraries of models

A comparative study on methods for reducing myopia of hill-climbing search in multirelational learning

Locally linear metric adaptation for semi-supervised clustering

A graphical model for protein secondary structure prediction

Take a walk and cluster genes: a TSP-based approach to optimal rearrangement clustering

Links between perceptrons, MLPs and SVMs

Communication complexity as a lower bound for learning in games

Distribution kernels based on moments of counts

A needle in a haystack: local one-class optimization

Large margin hierarchical classification

Training conditional random fields via gradient tree boosting

K-means clustering via principal component analysis

Linearized cluster assignment via spectral ordering

The Bayesian backfitting relevance vector machine

Learning probabilistic motion models for mobile robots

Lookahead-based algorithms for anytime induction of decision trees

A Monte Carlo analysis of ensemble classification

Relational sequential inference with reliable observations

Solving cluster ensemble problems by bipartite graph partitioning

Delegating classifiers

A pitfall and solution in multi-class feature selection for text classification

Ensembles of nested dichotomies for multi-class problems

A fast iterative algorithm for fisher discriminant using heterogeneous kernels

Text categorization with many redundant features: using aggressive feature selection to make SVMs competitive with C4.5

A MFoM learning approach to robust multiclass multi-label text categorization

Margin based feature selection - theory and algorithms

Tractable learning of large Bayes net structures from sparse data

Parameter space exploration with Gaussian process trees

Learning Bayesian network classifiers by maximizing conditional likelihood

A kernel view of the dimensionality reduction of manifolds

A theoretical characterization of linear SVM-based feature selection

Optimising area under the ROC curve using gradient descent

Boosting margin based distance functions for clustering

Learning large margin classifiers locally and globally

Testing the significance of attribute interactions

Learning and discovery of predictive state representations in dynamical systems with reset

Boosting grammatical inference with confidence oracles

Multi-task feature and kernel selection for SVMs

A spatio-temporal extension to Isomap nonlinear dimension reduction

Robust feature induction for support vector machines

Kernel-based discriminative learning algorithms for labeling sequences, trees, and graphs

Bellman goes relational

Gradient LASSO for feature selection

Sparse cooperative Q-learning

Authorship verification as a one-class classification problem

Leveraging the margin more carefully

Kernel conditional random fields: representation and clique selection

Learning to learn with the informative vector machine

Hyperplane margin classifiers on the multinomial manifold

Probabilistic tangent subspace: a unified view

Entropy-based criterion in categorical clustering

Decision trees with minimal costs

Extensions of marginalized graph kernels

Dynamic abstraction in reinforcement learning via clustering

Bias and variance in value function estimation

The multiple multiplicative factor model for collaborative filtering

Diverse ensembles for active learning

Convergence of synchronous reinforcement learning with linear function approximation

Learning to fly by combining reinforcement learning with behavioural cloning

Learning first-order rules from data with multiple parts: applications on mining chemical compound data

Feature selection, $L_{1}$ vs. $L_{2}$ regularization, and rotational invariance

Active learning using pre-clustering

Decentralized detection and classification using kernel methods

Learning with non-positive kernels

Sequential information bottleneck for finite data

A maximum entropy approach to species distribution modeling

Incremental learning of linear model trees

Predictive automatic relevance determination by expectation propagation

Sequential skewing: an improved skewing algorithm

Learning to cluster using local neighborhood structure

Learning low dimensional predictive representations

Model selection via the AUC

Towards tight bounds for rule learning

Adaptive cognitive orthotics: combining reinforcement learning and constraint-based temporal reasoning

Online learning of conditionally I.I.D. data

Coalition calculation in a dynamic agent environment

Online and batch learning of pseudo-metrics

Using relative novelty to identify useful temporal abstractions in reinforcement learning

Generative modeling for continuous non-linearly embedded visual inference

Efficient hierarchical MCMC for policy search

Automated hierarchical mixtures of probabilistic principal component analyzers

Dynamic conditional random fields: factorized probabilistic models for labeling and segmenting sequence data

Interpolation-based Q-learning

SVM-based generalized multiple-instance learning via approximate box counting

Learning associative Markov networks

Learning random walk models for inducing word dependency distributions

Support vector machine learning for interdependent and structured output spaces

A hierarchical method for multi-class support vector machines

Learning a kernel matrix for nonlinear dimensionality reduction

Approximate inference by Markov chains on union spaces

Utile distinction hidden Markov models

P3VI: a partitioned, prioritized, parallel value iterator

Improving SVM accuracy by training on auxiliary data sources

Bayesian haplo-type inference via the dirichlet process

Generalized low rank approximations of matrices

Feature extraction via generalized uncorrelated linear discriminant analysis

Learning and evaluating classifiers under sample selection bias

Probabilistic score estimation with piecewise logistic regression

Solving large scale linear prediction problems using stochastic gradient descent algorithms

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

Bayesian inference for transductive learning of kernel matrix using the Tanner-Wong data augmentation algorithm

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