MLJ 2008

48 papers

A Bias/variance Decomposition for Models Using Collective Inference Jennifer Neville, David D. Jensen

A Collaborative Filtering Framework Based on Both Local User Similarity and Global User Similarity Heng Luo, Changyong Niu, Ruimin Shen, Carsten Ullrich

A Critical Analysis of Variants of the AUC Stijn Vanderlooy, Eyke Hüllermeier

A Formal Framework and Extensions for Function Approximation in Learning Classifier Systems Jan Drugowitsch, Alwyn Barry

A K -norm Pruning Algorithm for Decision Tree Classifiers Based on Error Rate Estimation Mingyu Zhong, Michael Georgiopoulos, Georgios C. Anagnostopoulos

A Linear Fit Gets the Correct Monotonicity Directions Malik Magdon-Ismail, Joseph Sill

A Notion of Task Relatedness Yielding Provable Multiple-Task Learning Guarantees Shai Ben-David, Reba Schuller Borbely

A Theory of Learning with Similarity Functions Maria-Florina Balcan, Avrim Blum, Nathan Srebro

Aggregation by Exponential Weighting, Sharp PAC-Bayesian Bounds and Sparsity Arnak S. Dalalyan, Alexandre B. Tsybakov

ALLPAD: Approximate Learning of Logic Programs with Annotated Disjunctions Fabrizio Riguzzi

Boosted Bayesian Network Classifiers Yushi Jing, Vladimir Pavlovic, James M. Rehg

Compressing Probabilistic Prolog Programs Luc De Raedt, Kristian Kersting, Angelika Kimmig, Kate Revoredo, Hannu Toivonen

Convex Multi-Task Feature Learning Andreas Argyriou, Theodoros Evgeniou, Massimiliano Pontil

Decision Trees for Hierarchical Multi-Label Classification Celine Vens, Jan Struyf, Leander Schietgat, Saso Dzeroski, Hendrik Blockeel

Discovering Significant Patterns Geoffrey I. Webb

Feature Selection via Sensitivity Analysis of SVM Probabilistic Outputs Kai Quan Shen, Chong Jin Ong, Xiaoping Li, Einar P. V. Wilder-Smith

Flexible Latent Variable Models for Multi-Task Learning Jian Zhang, Zoubin Ghahramani, Yiming Yang

Generalized Ordering-Search for Learning Directed Probabilistic Logical Models Jan Ramon, Tom Croonenborghs, Daan Fierens, Hendrik Blockeel, Maurice Bruynooghe

Improved MCMC Sampling Methods for Estimating Weighted Sums in Winnow with Application to DNF Learning Qingping Tao, Stephen D. Scott

Improving Maximum Margin Matrix Factorization Markus Weimer, Alexandros Karatzoglou, Alexander J. Smola

Improving the Structure MCMC Sampler for Bayesian Networks by Introducing a New Edge Reversal Move Marco Grzegorczyk, Dirk Husmeier

Incorporating Prior Knowledge in Support Vector Regression Fabien Lauer, Gérard Bloch

Incremental Exemplar Learning Schemes for Classification on Embedded Devices Ankur Jain, Daniel Nikovski

Inductive Logic Programming for Gene Regulation Prediction Sebastian Fröhler, Stefan Kramer

Inductive Process Modeling Will Bridewell, Pat Langley, Ljupco Todorovski, Saso Dzeroski

Inductive Transfer with Context-Sensitive Neural Networks Daniel L. Silver, Ryan Poirier, Duane Currie

Joint Feature Re-Extraction and Classification Using an Iterative Semi-Supervised Support Vector Machine Algorithm Yuanqing Li, Cuntai Guan

Large Margin vs. Large Volume in Transductive Learning Ran El-Yaniv, Dmitry Pechyony, Vladimir Vapnik

Layered Critical Values: A Powerful Direct-Adjustment Approach to Discovering Significant Patterns Geoffrey I. Webb

Learning ( K , L )-Contextual Tree Languages for Information Extraction from Web Pages Stefan Raeymaekers, Maurice Bruynooghe, Jan Van den Bussche

Learning Large-Alphabet and Analog Circuits with Value Injection Queries Dana Angluin, James Aspnes, Jiang Chen, Lev Reyzin

Learning Near-Optimal Policies with Bellman-Residual Minimization Based Fitted Policy Iteration and a Single Sample Path András Antos, Csaba Szepesvári, Rémi Munos

Learning Probabilistic Logic Models from Probabilistic Examples Jianzhong Chen, Stephen H. Muggleton, José Carlos Almeida Santos

Learning the Structure of Dynamic Bayesian Networks from Time Series and Steady State Measurements Harri Lähdesmäki, Ilya Shmulevich

Learning to Assign Degrees of Belief in Relational Domains Frédéric Koriche

Margin-Based First-Order Rule Learning Ulrich Rückert, Stefan Kramer

Multilabel Classification via Calibrated Label Ranking Johannes Fürnkranz, Eyke Hüllermeier, Eneldo Loza Mencía, Klaus Brinker

New Closed-Form Bounds on the Partition Function Krishnamurthy Dvijotham, Soumen Chakrabarti, Subhasis Chaudhuri

On Reoptimizing Multi-Class Classifiers Chris Bourke, Kun Deng, Stephen D. Scott, Robert E. Schapire, N. V. Vinodchandran

On the Connection Between the Phase Transition of the Covering Test and the Learning Success Rate in ILP Érick Alphonse, Aomar Osmani

QG/GA: A Stochastic Search for Progol Stephen H. Muggleton, Alireza Tamaddoni-Nezhad

Regret to the Best vs. Regret to the Average Eyal Even-Dar, Michael J. Kearns, Yishay Mansour, Jennifer Wortman

Robust Reductions from Ranking to Classification Maria-Florina Balcan, Nikhil Bansal, Alina Beygelzimer, Don Coppersmith, John Langford, Gregory B. Sorkin

Rollout Sampling Approximate Policy Iteration Christos Dimitrakakis, Michail G. Lagoudakis

Sketching Information Divergences Sudipto Guha, Piotr Indyk, Andrew McGregor

Structured Machine Learning: The Next Ten Years Thomas G. Dietterich, Pedro M. Domingos, Lise Getoor, Stephen H. Muggleton, Prasad Tadepalli

Transfer in Variable-Reward Hierarchical Reinforcement Learning Neville Mehta, Sriraam Natarajan, Prasad Tadepalli, Alan Fern

U-Shaped, Iterative, and Iterative-with-Counter Learning John Case, Samuel E. Moelius