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JMLR
2006
145views more  JMLR 2006»
9 years 11 months ago
Ensemble Pruning Via Semi-definite Programming
An ensemble is a group of learning models that jointly solve a problem. However, the ensembles generated by existing techniques are sometimes unnecessarily large, which can lead t...
Yi Zhang 0006, Samuel Burer, W. Nick Street
JMLR
2006
143views more  JMLR 2006»
9 years 11 months ago
Geometric Variance Reduction in Markov Chains: Application to Value Function and Gradient Estimation
We study a sequential variance reduction technique for Monte Carlo estimation of functionals in Markov Chains. The method is based on designing sequential control variates using s...
Rémi Munos
JMLR
2006
108views more  JMLR 2006»
9 years 11 months ago
Learning Spectral Clustering, With Application To Speech Separation
Spectral clustering refers to a class of techniques which rely on the eigenstructure of a similarity matrix to partition points into disjoint clusters, with points in the same clu...
Francis R. Bach, Michael I. Jordan
JMLR
2006
99views more  JMLR 2006»
9 years 11 months ago
Worst-Case Analysis of Selective Sampling for Linear Classification
A selective sampling algorithm is a learning algorithm for classification that, based on the past observed data, decides whether to ask the label of each new instance to be classi...
Nicolò Cesa-Bianchi, Claudio Gentile, Luca ...
JMLR
2006
90views more  JMLR 2006»
9 years 11 months ago
Superior Guarantees for Sequential Prediction and Lossless Compression via Alphabet Decomposition
We present worst case bounds for the learning rate of a known prediction method that is based on hierarchical applications of binary context tree weighting (CTW) predictors. A heu...
Ron Begleiter, Ran El-Yaniv
JMLR
2006
103views more  JMLR 2006»
9 years 11 months ago
On Model Selection Consistency of Lasso
Sparsity or parsimony of statistical models is crucial for their proper interpretations, as in sciences and social sciences. Model selection is a commonly used method to find such...
Peng Zhao, Bin Yu
JMLR
2006
116views more  JMLR 2006»
9 years 11 months ago
Step Size Adaptation in Reproducing Kernel Hilbert Space
This paper presents an online support vector machine (SVM) that uses the stochastic meta-descent (SMD) algorithm to adapt its step size automatically. We formulate the online lear...
S. V. N. Vishwanathan, Nicol N. Schraudolph, Alex ...
JMLR
2006
143views more  JMLR 2006»
9 years 11 months ago
Consistency and Convergence Rates of One-Class SVMs and Related Algorithms
We determine the asymptotic behaviour of the function computed by support vector machines (SVM) and related algorithms that minimize a regularized empirical convex loss function i...
Régis Vert, Jean-Philippe Vert
JMLR
2006
140views more  JMLR 2006»
9 years 11 months ago
Active Learning in Approximately Linear Regression Based on Conditional Expectation of Generalization Error
The goal of active learning is to determine the locations of training input points so that the generalization error is minimized. We discuss the problem of active learning in line...
Masashi Sugiyama
JMLR
2006
105views more  JMLR 2006»
9 years 11 months ago
Parallel Software for Training Large Scale Support Vector Machines on Multiprocessor Systems
Parallel software for solving the quadratic program arising in training support vector machines for classification problems is introduced. The software implements an iterative dec...
Luca Zanni, Thomas Serafini, Gaetano Zanghirati
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