| On temporal evolution in data streams | p. 1 |
| The future of CiteSeer : CiteSeer[superscript x] | p. 2 |
| Learning to have fun | p. 3 |
| Winning the DARPA grand challenge | p. 4 |
| Challenges of urban sensing | p. 5 |
| Learning in one-shot strategic form games | p. 6 |
| A selective sampling strategy for label ranking | p. 18 |
| Combinatorial Markov random fields | p. 30 |
| Learning stochastic tree edit distance | p. 42 |
| Pertinent background knowledge for learning protein grammars | p. 54 |
| Improving Bayesian network structure search with random variable aggregation hierarchies | p. 66 |
| Sequence discrimination using phase-type distributions | p. 78 |
| Languages as hyperplanes : grammatical inference with string kernels | p. 90 |
| Toward robust real-world inference : a new perspective on explanation-based learning | p. 102 |
| Fisher kernels for relational data | p. 114 |
| Evaluating misclassifications in imbalanced data | p. 126 |
| Improving control-knowledge acquisition for planning by active learning | p. 138 |
| PAC-learning of Markov models with hidden state | p. 150 |
| A discriminative approach for the retrieval of images from text queries | p. 162 |
| TildeCRF : conditional random fields for logical sequences | p. 174 |
| Unsupervised multiple-instance learning for functional profiling of genomic data | p. 186 |
| Bayesian learning of Markov network structure | p. 198 |
| Approximate policy iteration for closed-loop learning of visual tasks | p. 210 |
| Task-driven discretization of the joint space of visual percepts and continuous actions | p. 222 |
| EM algorithm for symmetric causal independence models | p. 234 |
| Deconvolutive clustering of Markov states | p. 246 |
| Patching approximate solutions in reinforcement learning | p. 258 |
| Fast variational inference for Gaussian process models through KL-correction | p. 270 |
| Bandit based Monte-Carlo planning | p. 282 |
| Bayesian learning with mixtures of trees | p. 294 |
| Transductive Gaussian process regression with automatic model selection | p. 306 |
| Efficient convolution kernels for dependency and constituent syntactic trees | p. 318 |
| Why is rule learning optimistic and how to correct it | p. 330 |
| Automatically evolving rule induction algorithms | p. 341 |
| Bayesian active learning for sensitivity analysis | p. 353 |
| Mixtures of Kikucki approximations | p. 365 |
| Boosting in PN spaces | p. 377 |
| Prioritizing point-based POMDP solvers | p. 389 |
| Graph based semi-supervised learning with sharper edges | p. 401 |
| Margin-based active learning for structured output spaces | p. 413 |
| Skill acquisition via transfer learning and advice taking | p. 425 |
| Constant rate approximate maximum margin algorithms | p. 437 |
| Batch classification with applications in computer aided diagnosis | p. 449 |
| Improving the ranking performance of decision trees | p. 461 |
| Multiple-instance learning via random walk | p. 473 |
| Localized alternative cluster ensembles for collaborative structuring | p. 485 |
| Distributional features for text categorization | p. 497 |
| Subspace metric ensembles for semi-supervised clustering of high dimensional data | p. 509 |
| An adaptive kernel method for semi-supervised clustering | p. 521 |
| To select or to weigh : a comparative study of model selection and model weighing for SPODE ensembles | p. 533 |
| Ensembles of nearest neighbor forecasts | p. 545 |
| Learning process models with missing data | p. 557 |
| Case-based label ranking | p. 566 |
| Cascade evaluation of clustering algorithms | p. 574 |
| Making good probability estimates for regression | p. 582 |
| Fast spectral clustering of data using sequential matrix compression | p. 590 |
| An information-theoretic framework for high-order co-clustering of heterogeneous objects | p. 598 |
| Efficient inference in large conditional random fields | p. 606 |
| A kernel-based approach to estimating phase shifts between irregularly sampled time series : an application to gravitational lenses | p. 614 |
| Cost-sensitive decision tree learning for forensic classification | p. 622 |
| The minimum volume covering ellipsoid estimation in kernel-defined feature spaces | p. 630 |
| Right of inference : nearest rectangle learning revisited | p. 638 |
| Reinforcement learning for MDPs with constraints | p. 646 |
| Efficient non-linear control through neuroevolution | p. 654 |
| Efficient prediction-based validation for document clustering | p. 663 |
| On testing the missing at random assumption | p. 671 |
| B-matching for spectral clustering | p. 679 |
| Multi-class ensemble-based active learning | p. 687 |
| Active learning with irrelevant examples | p. 695 |
| Classification with support hyperplanes | p. 703 |
| (Agnostic) PAC learning concepts in higher-order logic | p. 711 |
| Evaluating feature selection for SVMs in high dimensions | p. 719 |
| Revisiting Fisher kernels for document similarities | p. 727 |
| Scaling model-based average-reward reinforcement learning for product delivery | p. 735 |
| Robust probabilistic calibration | p. 743 |
| Missing data in kernel PCA | p. 751 |
| Exploiting extremely rare features in text categorization | p. 759 |
| Efficient large scale linear programming support vector machines | p. 767 |
| An efficient approximation to lookahead in relational learners | p. 775 |
| Improvement of systems management policies using hybrid reinforcement learning | p. 783 |
| Diversified SVM ensembles for large data sets | p. 792 |
| Dynamic integration with random forests | p. 801 |
| Bagging using statistical queries | p. 809 |
| Guiding the search in the NO region of the phase transition problem with a partial subsumption test | p. 817 |
| Spline embedding for nonlinear dimensionality reduction | p. 825 |
| Cost-sensitive learning of SVM for ranking | p. 833 |
| Variational Bayesian Dirichlet-multinomial allocation for exponential family mixtures | p. 841 |
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