Abstract is missing.
- Distribution kernels based on moments of countsCorinna Cortes, Mehryar Mohri. [doi]
- Lookahead-based algorithms for anytime induction of decision treesSaher Esmeir, Shaul Markovitch. [doi]
- Online learning of conditionally I.I.D. dataDaniil Ryabko. [doi]
- Take a walk and cluster genes: a TSP-based approach to optimal rearrangement clusteringSharlee Climer, Weixiong Zhang. [doi]
- A fast iterative algorithm for fisher discriminant using heterogeneous kernelsGlenn Fung, Murat Dundar, Jinbo Bi, R. Bharat Rao. [doi]
- Diverse ensembles for active learningPrem Melville, Raymond J. Mooney. [doi]
- Delegating classifiersCésar Ferri, Peter A. Flach, José Hernández-Orallo. [doi]
- P3VI: a partitioned, prioritized, parallel value iteratorDavid Wingate, Kevin D. Seppi. [doi]
- Towards tight bounds for rule learningUlrich Rückert, Stefan Kramer. [doi]
- Hyperplane margin classifiers on the multinomial manifoldGuy Lebanon, John D. Lafferty. [doi]
- Approximate inference by Markov chains on union spacesMax Welling, Michal Rosen-Zvi, Yee Whye Teh. [doi]
- A MFoM learning approach to robust multiclass multi-label text categorizationSheng Gao, Wen Wu, Chin-Hui Lee, Tat-Seng Chua. [doi]
- Tractable learning of large Bayes net structures from sparse dataAnna Goldenberg, Andrew Moore. [doi]
- Learning to learn with the informative vector machineNeil D. Lawrence, John C. Platt. [doi]
- Incremental learning of linear model treesDuncan Potts. [doi]
- Multi-task feature and kernel selection for SVMsTony Jebara. [doi]
- Optimising area under the ROC curve using gradient descentAlan Herschtal, Bhavani Raskutti. [doi]
- Generative modeling for continuous non-linearly embedded visual inferenceCristian Sminchisescu, Allan D. Jepson. [doi]
- Improving SVM accuracy by training on auxiliary data sourcesPengcheng Wu, Thomas G. Dietterich. [doi]
- Generalized low rank approximations of matricesJieping Ye. [doi]
- Learning first-order rules from data with multiple parts: applications on mining chemical compound dataCholwich Nattee, Sukree Sinthupinyo, Masayuki Numao, Takashi Okada. [doi]
- Learning low dimensional predictive representationsMatthew Rosencrantz, Geoffrey J. Gordon, Sebastian Thrun. [doi]
- A pitfall and solution in multi-class feature selection for text classificationGeorge Forman. [doi]
- Gaussian process classification for segmenting and annotating sequencesYasemin Altun, Thomas Hofmann, Alex J. Smola. [doi]
- Links between perceptrons, MLPs and SVMsRonan Collobert, Samy Bengio. [doi]
- Nonparametric classification with polynomial MPMC cascadesSander M. Bohte, Markus Breitenbach, Gregory Z. Grudic. [doi]
- Model selection via the AUCSaharon Rosset. [doi]
- A comparative study on methods for reducing myopia of hill-climbing search in multirelational learningLourdes Peña Castillo, Stefan Wrobel. [doi]
- Margin based feature selection - theory and algorithmsRan Gilad-Bachrach, Amir Navot, Naftali Tishby. [doi]
- Bayesian haplo-type inference via the dirichlet processEric P. Xing, Roded Sharan, Michael I. Jordan. [doi]
- A theoretical characterization of linear SVM-based feature selectionDouglas Hardin, Ioannis Tsamardinos, Constantin F. Aliferis. [doi]
- Decentralized detection and classification using kernel methodsXuanLong Nguyen, Martin J. Wainwright, Michael I. Jordan. [doi]
- Learning and discovery of predictive state representations in dynamical systems with resetMichael R. James, Satinder P. Singh. [doi]
- Robust feature induction for support vector machinesRong Jin, Huan Liu. [doi]
- Training conditional random fields via gradient tree boostingThomas G. Dietterich, Adam Ashenfelter, Yaroslav Bulatov. [doi]
- Learning to fly by combining reinforcement learning with behavioural cloningEduardo F. Morales, Claude Sammut. [doi]
- An information theoretic analysis of maximum likelihood mixture estimation for exponential familiesArindam Banerjee, Inderjit S. Dhillon, Joydeep Ghosh, Srujana Merugu. [doi]
- A graphical model for protein secondary structure predictionWei Chu, Zoubin Ghahramani, David L. Wild. [doi]
- Efficient hierarchical MCMC for policy searchMalcolm J. A. Strens. [doi]
- Linearized cluster assignment via spectral orderingChris H. Q. Ding, Xiaofeng He. [doi]
- Learning large margin classifiers locally and globallyKaizhu Huang, Haiqin Yang, Irwin King, Michael R. Lyu. [doi]
- Entropy-based criterion in categorical clusteringTao Li, Sheng Ma, Mitsunori Ogihara. [doi]
- Using relative novelty to identify useful temporal abstractions in reinforcement learningÖzgür Simsek, Andrew G. Barto. [doi]
- Authorship verification as a one-class classification problemMoshe Koppel, Jonathan Schler. [doi]
- Decision trees with minimal costsCharles X. Ling, Qiang Yang, Jianning Wang, Shichao Zhang. [doi]
- Bias and variance in value function estimationShie Mannor, Duncan Simester, Peng Sun, John N. Tsitsiklis. [doi]
- Coalition calculation in a dynamic agent environmentTed Scully, Michael G. Madden, Gerard Lyons. [doi]
- Redundant feature elimination for multi-class problemsAnnalisa Appice, Michelangelo Ceci, Simon Rawles, Peter A. Flach. [doi]
- Semi-supervised learning using randomized mincutsAvrim Blum, John D. Lafferty, Mugizi Robert Rwebangira, Rajashekar Reddy. [doi]
- Integrating constraints and metric learning in semi-supervised clusteringMikhail Bilenko, Sugato Basu, Raymond J. Mooney. [doi]
- Bayesian inference for transductive learning of kernel matrix using the Tanner-Wong data augmentation algorithmZhihua Zhang, Dit-Yan Yeung, James T. Kwok. [doi]
- Unifying collaborative and content-based filteringJustin Basilico, Thomas Hofmann. [doi]
- Convergence of synchronous reinforcement learning with linear function approximationArtur Merke, Ralf Schoknecht. [doi]
- Kernel conditional random fields: representation and clique selectionJohn D. Lafferty, Xiaojin Zhu, Yan Liu. [doi]
- Gradient LASSO for feature selectionYongdai Kim, Jinseog Kim. [doi]
- Utile distinction hidden Markov modelsDaan Wierstra, Marco Wiering. [doi]
- Apprenticeship learning via inverse reinforcement learningPieter Abbeel, Andrew Y. Ng. [doi]
- Surrogate maximization/minimization algorithms for AdaBoost and the logistic regression modelZhihua Zhang, James T. Kwok, Dit-Yan Yeung. [doi]
- Multiple kernel learning, conic duality, and the SMO algorithmFrancis R. Bach, Gert R. G. Lanckriet, Michael I. Jordan. [doi]
- Boosting grammatical inference with confidence oraclesJean-Christophe Janodet, Richard Nock, Marc Sebban, Henri-Maxime Suchier. [doi]
- Variational methods for the Dirichlet processDavid M. Blei, Michael I. Jordan. [doi]
- Parameter space exploration with Gaussian process treesRobert B. Gramacy, Herbert K. H. Lee, William G. Macready. [doi]
- Learning and evaluating classifiers under sample selection biasBianca Zadrozny. [doi]
- A hierarchical method for multi-class support vector machinesVolkan Vural, Jennifer G. Dy. [doi]
- Bellman goes relationalKristian Kersting, Martijn Van Otterlo, Luc De Raedt. [doi]
- Large margin hierarchical classificationOfer Dekel, Joseph Keshet, Yoram Singer. [doi]
- A needle in a haystack: local one-class optimizationKoby Crammer, Gal Chechik. [doi]
- A spatio-temporal extension to Isomap nonlinear dimension reductionOdest Chadwicke Jenkins, Maja J. Mataric. [doi]
- A kernel view of the dimensionality reduction of manifoldsJihun Ham, Daniel D. Lee, Sebastian Mika, Bernhard Schölkopf. [doi]
- ::::K::::-means clustering via principal component analysisChris H. Q. Ding, Xiaofeng He. [doi]
- A multiplicative up-propagation algorithmJong-Hoon Ahn, Seungjin Choi, Jong-Hoon Oh. [doi]
- Learning to track 3D human motion from silhouettesAnkur Agarwal, Bill Triggs. [doi]
- The Bayesian backfitting relevance vector machineAaron D Souza, Sethu Vijayakumar, Stefan Schaal. [doi]
- Ensembles of nested dichotomies for multi-class problemsEibe Frank, Stefan Kramer. [doi]
- Boosting margin based distance functions for clusteringTomer Hertz, Aharon Bar-Hillel, Daphna Weinshall. [doi]
- Feature extraction via generalized uncorrelated linear discriminant analysisJieping Ye, Ravi Janardan, Qi Li, Haesun Park. [doi]
- Adaptive cognitive orthotics: combining reinforcement learning and constraint-based temporal reasoningMatthew R. Rudary, Satinder P. Singh, Martha E. Pollack. [doi]
- Support vector machine learning for interdependent and structured output spacesIoannis Tsochantaridis, Thomas Hofmann, Thorsten Joachims, Yasemin Altun. [doi]
- Sparse cooperative Q-learningJelle R. Kok, Nikos A. Vlassis. [doi]
- Active learning using pre-clusteringHieu Tat Nguyen, Arnold W. M. Smeulders. [doi]
- Ensemble selection from libraries of modelsRich Caruana, Alexandru Niculescu-Mizil, Geoff Crew, Alex Ksikes. [doi]
- Predictive automatic relevance determination by expectation propagationYuan (Alan) Qi, Thomas P. Minka, Rosalind W. Picard, Zoubin Ghahramani. [doi]
- Automated hierarchical mixtures of probabilistic principal component analyzersTing Su, Jennifer G. Dy. [doi]
- Dynamic abstraction in reinforcement learning via clusteringShie Mannor, Ishai Menache, Amit Hoze, Uri Klein. [doi]
- Solving large scale linear prediction problems using stochastic gradient descent algorithmsTong Zhang. [doi]
- Online and batch learning of pseudo-metricsShai Shalev-Shwartz, Yoram Singer, Andrew Y. Ng. [doi]
- Relational sequential inference with reliable observationsAlan Fern, Robert Givan. [doi]
- Probabilistic score estimation with piecewise logistic regressionJian Zhang, Yiming Yang. [doi]
- Interpolation-based Q-learningCsaba Szepesvári, William D. Smart. [doi]
- Active learning of label ranking functionsKlaus Brinker. [doi]
- Co-EM support vector learningUlf Brefeld, Tobias Scheffer. [doi]
- Communication complexity as a lower bound for learning in gamesVincent Conitzer, Tuomas Sandholm. [doi]
- Learning probabilistic motion models for mobile robotsAustin I. Eliazar, Ronald Parr. [doi]
- Learning to cluster using local neighborhood structureRómer Rosales, Kannan Achan, Brendan J. Frey. [doi]
- C4.5 competence map: a phase transition-inspired approachNicolas Baskiotis, Michèle Sebag. [doi]
- Sequential skewing: an improved skewing algorithmSoumya Ray, David Page. [doi]
- Kernel-based discriminative learning algorithms for labeling sequences, trees, and graphsHisashi Kashima, Yuta Tsuboi. [doi]
- Testing the significance of attribute interactionsAleks Jakulin, Ivan Bratko. [doi]
- Feature subset selection for learning preferences: a case studyAntonio Bahamonde, Gustavo F. Bayón, Jorge Díez, José Ramón Quevedo, Oscar Luaces, Juan José del Coz, Jaime Alonso, Félix Goyache. [doi]
- Extensions of marginalized graph kernelsPierre Mahé, Nobuhisa Ueda, Tatsuya Akutsu, Jean-Luc Perret, Jean-Philippe Vert. [doi]
- Dynamic conditional random fields: factorized probabilistic models for labeling and segmenting sequence dataCharles A. Sutton, Khashayar Rohanimanesh, Andrew McCallum. [doi]
- Estimating replicability of classifier learning experimentsRemco R. Bouckaert. [doi]
- Sequential information bottleneck for finite dataJaakko Peltonen, Janne Sinkkonen, Samuel Kaski. [doi]
- Leveraging the margin more carefullyNir Krause, Yoram Singer. [doi]
- Learning associative Markov networksBenjamin Taskar, Vassil Chatalbashev, Daphne Koller. [doi]
- Learning random walk models for inducing word dependency distributionsKristina Toutanova, Christopher D. Manning, Andrew Y. Ng. [doi]
- SVM-based generalized multiple-instance learning via approximate box countingQingping Tao, Stephen D. Scott, N. V. Vinodchandran, Thomas Takeo Osugi. [doi]
- A maximum entropy approach to species distribution modelingSteven J. Phillips, Miroslav Dudík, Robert E. Schapire. [doi]
- Probabilistic tangent subspace: a unified viewJianguo Lee, Jingdong Wang, Changshui Zhang, Zhaoqi Bian. [doi]
- Text categorization with many redundant features: using aggressive feature selection to make SVMs competitive with C4.5Evgeniy Gabrilovich, Shaul Markovitch. [doi]
- Locally linear metric adaptation for semi-supervised clusteringHong Chang, Dit-Yan Yeung. [doi]
- A Monte Carlo analysis of ensemble classificationRoberto Esposito, Lorenza Saitta. [doi]
- Solving cluster ensemble problems by bipartite graph partitioningXiaoli Zhang Fern, Carla E. Brodley. [doi]
- Learning a kernel matrix for nonlinear dimensionality reductionKilian Q. Weinberger, Fei Sha, Lawrence K. Saul. [doi]
- Learning Bayesian network classifiers by maximizing conditional likelihoodDaniel Grossman, Pedro Domingos. [doi]
- The multiple multiplicative factor model for collaborative filteringBenjamin Marlin, Richard S. Zemel. [doi]
- Learning with non-positive kernelsCheng Soon Ong, Xavier Mary, Stéphane Canu, Alexander J. Smola. [doi]