MLJ 2009

49 papers

A Self-Training Approach to Cost Sensitive Uncertainty Sampling Alexander Liu, Goo Jun, Joydeep Ghosh

An Algebraic Characterization of the Optimum of Regularized Kernel Methods Francesco Dinuzzo, Giuseppe De Nicolao

An Efficient Algorithm for Learning to Rank from Preference Graphs Tapio Pahikkala, Evgeni Tsivtsivadze, Antti Airola, Jouni Järvinen, Jorma Boberg

An Investigation into Feature Construction to Assist Word Sense Disambiguation Lucia Specia, Ashwin Srinivasan, Sachindra Joshi, Ganesh Ramakrishnan, Maria das Graças Volpe Nunes

Anytime Classification for a Pool of Instances Bei Hui, Ying Yang, Geoffrey I. Webb

Bayesian Learning of Graphical Vector Autoregressions with Unequal Lag-Lengths Pekka Marttinen, Jukka Corander

Brave Induction: A Logical Framework for Learning from Incomplete Information Chiaki Sakama, Katsumi Inoue

Cluster-Grouping: From Subgroup Discovery to Clustering Albrecht Zimmermann, Luc De Raedt

Combining Instance-Based Learning and Logistic Regression for Multilabel Classification Weiwei Cheng, Eyke Hüllermeier

Convergence Analysis of Kernel Canonical Correlation Analysis: Theory and Practice David R. Hardoon, John Shawe-Taylor

Cost-Sensitive Learning Based on Bregman Divergences Raúl Santos-Rodríguez, Alicia Guerrero-Curieses, Rocío Alaíz-Rodríguez, Jesús Cid-Sueiro

Cutting-Plane Training of Structural SVMs Thorsten Joachims, Thomas Finley, Chun-Nam John Yu

Discretization for Naive-Bayes Learning: Managing Discretization Bias and Variance Ying Yang, Geoffrey I. Webb

Effective Short-Term Opponent Exploitation in Simplified Poker Finnegan Southey, Bret Hoehn, Robert C. Holte

Efficient Covariance Matrix Update for Variable Metric Evolution Strategies Thorsten Suttorp, Nikolaus Hansen, Christian Igel

Exact Bootstrap K-Nearest Neighbor Learners Brian M. Steele

Finding Anomalous Periodic Time Series Umaa Rebbapragada, Pavlos Protopapas, Carla E. Brodley, Charles R. Alcock

gBoost: A Mathematical Programming Approach to Graph Classification and Regression Hiroto Saigo, Sebastian Nowozin, Tadashi Kadowaki, Taku Kudo, Koji Tsuda

Generalized Isotonic Conditional Random Fields Yi Mao, Guy Lebanon

Graph Kernels Based on Tree Patterns for Molecules Pierre Mahé, Jean-Philippe Vert

Guest Editorial: Special Issue on Structured Prediction Charles Parker, Yasemin Altun, Prasad Tadepalli

Hybrid Least-Squares Algorithms for Approximate Policy Evaluation Jeffrey Johns, Marek Petrik, Sridhar Mahadevan

Incremental Data-Driven Learning of a Novelty Detection Model for One-Class Classification with Application to High-Dimensional Noisy Data Randa Kassab, Frédéric Alexandre

Latent Grouping Models for User Preference Prediction Eerika Savia, Kai Puolamäki, Samuel Kaski

Learning Block-Preserving Graph Patterns and Its Application to Data Mining Hitoshi Yamasaki, Yosuke Sasaki, Takayoshi Shoudai, Tomoyuki Uchida, Yusuke Suzuki

Learning Multi-Linear Representations of Distributions for Efficient Inference Dan Roth, Rajhans Samdani

Local Procrustes for Manifold Embedding: A Measure of Embedding Quality and Embedding Algorithms Yair Goldberg, Yaacov Ritov

Matrix Representations, Linear Transformations, and Kernels for Disambiguation in Natural Language Tapio Pahikkala, Sampo Pyysalo, Jorma Boberg, Jouni Järvinen, Tapio Salakoski

Measuring Classifier Performance: A Coherent Alternative to the Area Under the ROC Curve David J. Hand

Mining Probabilistic Automata: A Statistical View of Sequential Pattern Mining Stéphanie Jacquemont, François Jacquenet, Marc Sebban

NP-Hardness of Euclidean Sum-of-Squares Clustering Daniel Aloise, Amit Deshpande, Pierre Hansen, Preyas Popat

On Structured Output Training: Hard Cases and an Efficient Alternative Thomas Gärtner, Shankar Vembu

On the Properties of Von Neumann Kernels for Link Analysis Masashi Shimbo, Takahiko Ito, Daichi Mochihashi, Yuji Matsumoto

Parallel ILP for Distributed-Memory Architectures Nuno A. Fonseca, Ashwin Srinivasan, Fernando M. A. Silva, Rui Camacho

Periodic Step-Size Adaptation in Second-Order Gradient Descent for Single-Pass On-Line Structured Learning Chun-Nan Hsu, Han-Shen Huang, Yu-Ming Chang, Yuh-Jye Lee

Piecewise Training for Structured Prediction Charles Sutton, Andrew McCallum

Pool-Based Active Learning in Approximate Linear Regression Masashi Sugiyama, Shinichi Nakajima

Search-Based Structured Prediction Hal Daumé Iii, John Langford, Daniel Marcu

Semi-Supervised Graph Clustering: A Kernel Approach Brian Kulis, Sugato Basu, Inderjit S. Dhillon, Raymond J. Mooney

Sparse Kernel SVMs via Cutting-Plane Training Thorsten Joachims, Chun-Nam John Yu

Structured Prediction by Joint Kernel Support Estimation Christoph H. Lampert, Matthew B. Blaschko

Structured Prediction with Reinforcement Learning Francis Maes, Ludovic Denoyer, Patrick Gallinari

The Generalization Performance of ERM Algorithm with Strongly Mixing Observations Bin Zou, Luoqing Li, Zongben Xu

The Lattice Structure and Refinement Operators for the Hypothesis Space Bounded by a Bottom Clause Alireza Tamaddoni-Nezhad, Stephen H. Muggleton

Training Parsers by Inverse Reinforcement Learning Gergely Neu, Csaba Szepesvári

Transfer Bounds for Linear Feature Learning Andreas Maurer

Tree-Structured Model Diagnostics for Linear Regression Xiaogang Su, Chih-Ling Tsai, Morgan C. Wang

Upper Bound for Variational Free Energy of Bayesian Networks Kazuho Watanabe, Motoki Shiga, Sumio Watanabe

Using the Bottom Clause and Mode Declarations in FOL Theory Revision from Examples Ana Luísa Duboc, Aline Paes, Gerson Zaverucha