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Journal of Machine Learning Research, Volume 8
Volume 8, 2007
- Nicolás García-Pedrajas, César Ignacio García-Osorio, Colin Fyfe:
Nonlinear Boosting Projections for Ensemble Construction. 1-33 - Ya Xue, Xuejun Liao, Lawrence Carin, Balaji Krishnapuram:
Multi-Task Learning for Classification with Dirichlet Process Priors. 35-63 - Marc Teboulle:
A Unified Continuous Optimization Framework for Center-Based Clustering Methods. 65-102 - Rocío Alaíz-Rodríguez, Alicia Guerrero-Curieses, Jesús Cid-Sueiro:
Minimax Regret Classifier for Imprecise Class Distributions. 103-130 - Nikolaj Tatti:
Distances between Data Sets Based on Summary Statistics. 131-154 - Tapani Raiko, Harri Valpola, Markus Harva, Juha Karhunen:
Building Blocks for Variational Bayesian Learning of Latent Variable Models. 155-201 - Sanjoy Dasgupta, Leonard J. Schulman
:
A Probabilistic Analysis of EM for Mixtures of Separated, Spherical Gaussians. 203-226 - Roni Khardon, Gabriel Wachman:
Noise Tolerant Variants of the Perceptron Algorithm. 227-248 - Yiming Ying, Ding-Xuan Zhou:
Learnability of Gaussians with Flexible Variances. 249-276 - Ariel Elbaz, Homin K. Lee, Rocco A. Servedio, Andrew Wan:
Separating Models of Learning from Correlated and Uncorrelated Data. 277-290 - Gaëlle Loosli, Stéphane Canu:
Comments on the "Core Vector Machines: Fast SVM Training on Very Large Data Sets". 291-301 - Nikolas List, Hans Ulrich Simon:
General Polynomial Time Decomposition Algorithms. 303-321 - Michael Biehl, Anarta Ghosh, Barbara Hammer:
Dynamics and Generalization Ability of LVQ Algorithms. 323-360 - Kenji Fukumizu, Francis R. Bach, Arthur Gretton:
Statistical Consistency of Kernel Canonical Correlation Analysis. 361-383 - Marco Reisert, Hans Burkhardt:
Learning Equivariant Functions with Matrix Valued Kernels. 385-408 - David Mease, Abraham J. Wyner, Andreas Buja:
Boosted Classification Trees and Class Probability/Quantile Estimation. 409-439 - Ryan M. Rifkin, Ross A. Lippert:
Value Regularization and Fenchel Duality. 441-479 - Niels Landwehr, Kristian Kersting, Luc De Raedt:
Integrating Naïve Bayes and FOIL. 481-507 - Sébastien Gadat, Laurent Younes:
A Stochastic Algorithm for Feature Selection in Pattern Recognition. 509-547 - Marta Arias, Roni Khardon, Jérôme Maloberti:
Learning Horn Expressions with LOGAN-H. 549-587 - Roland Nilsson, José M. Peña, Johan Björkegren, Jesper Tegnér:
Consistent Feature Selection for Pattern Recognition in Polynomial Time. 589-612 - Markus Kalisch, Peter Bühlmann:
Estimating High-Dimensional Directed Acyclic Graphs with the PC-Algorithm. 613-636 - Robert Tibshirani, Trevor Hastie:
Margin Trees for High-dimensional Classification. 637-652 - Jennifer Neville, David D. Jensen:
Relational Dependency Networks. 653-692 - Charles Sutton, Andrew McCallum, Khashayar Rohanimanesh:
Dynamic Conditional Random Fields: Factorized Probabilistic Models for Labeling and Segmenting Sequence Data. 693-723 - Kristen Grauman, Trevor Darrell:
The Pyramid Match Kernel: Efficient Learning with Sets of Features. 725-760 - Art B. Owen:
Infinitely Imbalanced Logistic Regression. 761-773 - Peter L. Bartlett, Ambuj Tewari:
Sparseness vs Estimating Conditional Probabilities: Some Asymptotic Results. 775-790 - Ofer Melnik, Yehuda Vardi, Cun-Hui Zhang:
Concave Learners for Rankboost. 791-812 - Shantanu Chakrabartty, Gert Cauwenberghs:
Gini Support Vector Machine: Quadratic Entropy Based Robust Multi-Class Probability Regression. 813-839 - Gavin C. Cawley, Nicola L. C. Talbot:
Preventing Over-Fitting during Model Selection via Bayesian Regularisation of the Hyper-Parameters. 841-861 - Jean-Yves Audibert, Olivier Bousquet:
Combining PAC-Bayesian and Generic Chaining Bounds. 863-889 - Saher Esmeir, Shaul Markovitch:
Anytime Learning of Decision Trees. 891-933 - Sofus A. Macskassy, Foster J. Provost:
Classification in Networked Data: A Toolkit and a Univariate Case Study. 935-983 - Masashi Sugiyama, Matthias Krauledat, Klaus-Robert Müller:
Covariate Shift Adaptation by Importance Weighted Cross Validation. 985-1005 - Ambuj Tewari, Peter L. Bartlett:
On the Consistency of Multiclass Classification Methods. 1007-1025 - Masashi Sugiyama:
Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis. 1027-1061 - Zoltán Szabó, Barnabás Póczos, András Lörincz:
Undercomplete Blind Subspace Deconvolution. 1063-1095 - Mads Dyrholm, Christoforos Christoforou, Lucas C. Parra:
Bilinear Discriminant Component Analysis. 1097-1111 - Joris M. Mooij, Hilbert J. Kappen:
Loop Corrections for Approximate Inference on Factor Graphs. 1113-1143 - Wei Pan, Xiaotong Shen:
Penalized Model-Based Clustering with Application to Variable Selection. 1145-1164 - Chao-Chun Liu, Dao-Qing Dai, Hong Yan:
Local Discriminant Wavelet Packet Coordinates for Face Recognition. 1165-1195 - Margarita Osadchy, Yann LeCun, Matthew L. Miller:
Synergistic Face Detection and Pose Estimation with Energy-Based Models. 1197-1215 - Miroslav Dudík, Steven J. Phillips, Robert E. Schapire:
Maximum Entropy Density Estimation with Generalized Regularization and an Application to Species Distribution Modeling. 1217-1260 - Moshe Koppel, Jonathan Schler, Elisheva Bonchek-Dokow:
Measuring Differentiability: Unmasking Pseudonymous Authors. 1261-1276 - Santosh Srivastava, Maya R. Gupta, Bela A. Frigyik:
Bayesian Quadratic Discriminant Analysis. 1277-1305 - Avrim Blum, Yishay Mansour:
From External to Internal Regret. 1307-1324 - Matthias Hein, Jean-Yves Audibert, Ulrike von Luxburg:
Graph Laplacians and their Convergence on Random Neighborhood Graphs. 1325-1368 - Philippe Rigollet:
Generalization Error Bounds in Semi-supervised Classification Under the Cluster Assumption. 1369-1392 - Jaime S. Cardoso, Joaquim F. Pinto da Costa:
Learning to Classify Ordinal Data: The Data Replication Method. 1393-1429 - Vitaly Feldman:
Attribute-Efficient and Non-adaptive Learning of Parities and DNF Expressions. 1431-1460 - François Laviolette, Mario Marchand:
PAC-Bayes Risk Bounds for Stochastic Averages and Majority Votes of Sample-Compressed Classifiers. 1461-1487 - Rie Johnson, Tong Zhang:
On the Effectiveness of Laplacian Normalization for Graph Semi-supervised Learning. 1489-1517 - Kwangmoo Koh, Seung-Jean Kim, Stephen P. Boyd:
An Interior-Point Method for Large-Scale l1-Regularized Logistic Regression. 1519-1555 - Iain Melvin, Eugene Ie, Jason Weston, William Stafford Noble, Christina S. Leslie:
Multi-class Protein Classification Using Adaptive Codes. 1557-1581 - Onur C. Hamsici, Aleix M. Martínez:
Spherical-Homoscedastic Distributions: The Equivalency of Spherical and Normal Distributions in Classification. 1583-1623 - Maytal Saar-Tsechansky, Foster J. Provost:
Handling Missing Values when Applying Classification Models. 1623-1657 - Marc Boullé:
Compression-Based Averaging of Selective Naive Bayes Classifiers. 1659-1685 - Jia Li, Surajit Ray, Bruce G. Lindsay:
A Nonparametric Statistical Approach to Clustering via Mode Identification. 1687-1723 - Alexander Clark, Rémi Eyraud:
Polynomial Identification in the Limit of Substitutable Context-free Languages. 1725-1745 - Ray J. Hickey:
Structure and Majority Classes in Decision Tree Learning. 1747-1768 - Natesh S. Pillai, Qiang Wu, Feng Liang, Sayan Mukherjee, Robert L. Wolpert:
Characterizing the Function Space for Bayesian Kernel Models. 1769-1797 - Gal Elidan, Iftach Nachman, Nir Friedman:
"Ideal Parent" Structure Learning for Continuous Variable Bayesian Networks. 1799-1833 - Manu Chhabra, Robert A. Jacobs, Daniel Stefankovic:
Behavioral Shaping for Geometric Concepts. 1835-1865 - Junhui Wang, Xiaotong Shen:
Large Margin Semi-supervised Learning. 1867-1891 - Simon Günter, Nicol N. Schraudolph, S. V. N. Vishwanathan:
Fast Iterative Kernel Principal Component Analysis. 1893-1918 - Arindam Banerjee, Inderjit S. Dhillon, Joydeep Ghosh, Srujana Merugu, Dharmendra S. Modha:
A Generalized Maximum Entropy Approach to Bregman Co-clustering and Matrix Approximation. 1919-1986 - Vicenç Gómez, Joris M. Mooij, Hilbert J. Kappen:
Truncating the Loop Series Expansion for Belief Propagation. 1987-2016 - Aggelos Chariatis:
Very Fast Online Learning of Highly Non Linear Problems. 2017-2045 - Dima Kuzmin, Manfred K. Warmuth:
Unlabeled Compression Schemes for Maximum Classes. 2047-2081 - Yuesheng Xu, Haizhang Zhang:
Refinable Kernels. 2083-2120 - Marco Loog:
A Complete Characterization of a Family of Solutions to a Generalized Fisher Criterion. 2121-2123 - Matthew E. Taylor, Peter Stone, Yaxin Liu:
Transfer Learning via Inter-Task Mappings for Temporal Difference Learning. 2125-2167 - Sridhar Mahadevan, Mauro Maggioni:
Proto-value Functions: A Laplacian Framework for Learning Representation and Control in Markov Decision Processes. 2169-2231 - Ofer Dekel, Philip M. Long, Yoram Singer:
Online Learning of Multiple Tasks with a Shared Loss. 2233-2264 - Amir Globerson, Gal Chechik, Fernando Pereira, Naftali Tishby:
Euclidean Embedding of Co-occurrence Data. 2265-2295 - Evgeniy Gabrilovich, Shaul Markovitch:
Harnessing the Expertise of 70, 000 Human Editors: Knowledge-Based Feature Generation for Text Categorization. 2297-2345 - Peter L. Bartlett, Mikhail Traskin:
AdaBoost is Consistent. 2347-2368 - András György, Tamás Linder, Gábor Lugosi, György Ottucsák:
The On-Line Shortest Path Problem Under Partial Monitoring. 2369-2403 - Guy Lebanon, Yi Mao, Joshua V. Dillon:
The Locally Weighted Bag of Words Framework for Document Representation. 2405-2441 - Sören Sonnenburg, Mikio L. Braun, Cheng Soon Ong, Samy Bengio, Léon Bottou, Geoffrey Holmes, Yann LeCun, Klaus-Robert Müller, Fernando Pereira, Carl Edward Rasmussen, Gunnar Rätsch, Bernhard Schölkopf, Alexander J. Smola, Pascal Vincent, Jason Weston, Robert C. Williamson:
The Need for Open Source Software in Machine Learning. 2443-2466 - Francesco Dinuzzo, Marta Neve, Giuseppe De Nicolao, Ugo Pietro Gianazza:
On the Representer Theorem and Equivalent Degrees of Freedom of SVR. 2467-2495 - Ping Li, Trevor Hastie, Kenneth Ward Church:
Nonlinear Estimators and Tail Bounds for Dimension Reduction in l1 Using Cauchy Random Projections. 2497-2532 - Zakria Hussain, François Laviolette, Mario Marchand, John Shawe-Taylor, S. Charles Brubaker, Matthew D. Mullin:
Revised Loss Bounds for the Set Covering Machine and Sample-Compression Loss Bounds for Imbalanced Data. 2533-2549 - Yann Guermeur:
VC Theory of Large Margin Multi-Category Classifiers. 2551-2594 - Marlon Núñez, Raúl Fidalgo, Rafael Morales Bueno:
Learning in Environments with Unknown Dynamics: Towards more Robust Concept Learners. 2595-2628 - Mohammad Ghavamzadeh, Sridhar Mahadevan:
Hierarchical Average Reward Reinforcement Learning. 2629-2669 - Stéphan Clémençon, Nicolas Vayatis:
Ranking the Best Instances. 2671-2699 - Peng Zhao, Bin Yu:
Stagewise Lasso. 2701-2726 - Carine Hue, Marc Boullé:
A New Probabilistic Approach in Rank Regression with Optimal Bayesian Partitioning. 2727-2754 - J. Zico Kolter, Marcus A. Maloof:
Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts. 2755-2790
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