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CVPR
2011
IEEE
8 years 6 months ago
TaylorBoost: First and Second-order Boosting Algorithms with Explicit Margin Control
A new family of boosting algorithms, denoted TaylorBoost, is proposed. It supports any combination of loss function and first or second order optimization, and includes classical...
Mohammad Saberian, Hamed Masnadi-Shirazi, Nuno Vas...
TNN
2010
155views Management» more  TNN 2010»
8 years 9 months ago
Incorporating the loss function into discriminative clustering of structured outputs
Clustering using the Hilbert Schmidt independence criterion (CLUHSIC) is a recent clustering algorithm that maximizes the dependence between cluster labels and data observations ac...
Wenliang Zhong, Weike Pan, James T. Kwok, Ivor W. ...
SAC
2010
ACM
8 years 9 months ago
Probabilistic relabelling strategies for the label switching problem in Bayesian mixture models
The label switching problem is caused by the likelihood of a Bayesian mixture model being invariant to permutations of the labels. The permutation can change multiple times betwee...
M. Sperrin, Thomas Jaki, E. Wit
INTERSPEECH
2010
8 years 9 months ago
Improvements of search error risk minimization in viterbi beam search for speech recognition
This paper describes improvements in a search error risk minimization approach to fast beam search for speech recognition. In our previous work, we proposed this approach to reduc...
Takaaki Hori, Shinji Watanabe, Atsushi Nakamura
ICDM
2009
IEEE
175views Data Mining» more  ICDM 2009»
9 years 8 days ago
Maximum Margin Clustering with Multivariate Loss Function
This paper presents a simple but powerful extension of the maximum margin clustering (MMC) algorithm that optimizes multivariate performance measure specifically defined for clust...
Bin Zhao, James Tin-Yau Kwok, Changshui Zhang
COLT
2010
Springer
9 years 16 days ago
Optimal Algorithms for Online Convex Optimization with Multi-Point Bandit Feedback
Bandit convex optimization is a special case of online convex optimization with partial information. In this setting, a player attempts to minimize a sequence of adversarially gen...
Alekh Agarwal, Ofer Dekel, Lin Xiao
TIT
1998
96views more  TIT 1998»
9 years 2 months ago
Sequential Prediction of Individual Sequences Under General Loss Functions
Abstract— We consider adaptive sequential prediction of arbitrary binary sequences when the performance is evaluated using a general loss function. The goal is to predict on each...
David Haussler, Jyrki Kivinen, Manfred K. Warmuth
ML
2002
ACM
140views Machine Learning» more  ML 2002»
9 years 2 months ago
A Probabilistic Framework for SVM Regression and Error Bar Estimation
In this paper, we elaborate on the well-known relationship between Gaussian Processes (GP) and Support Vector Machines (SVM) under some convex assumptions for the loss functions. ...
Junbin Gao, Steve R. Gunn, Chris J. Harris, Martin...
FUIN
2006
95views more  FUIN 2006»
9 years 2 months ago
Bounds for Validation
In this paper we derive the bounds for Validation (known also as Hold-Out Estimate and Train-and-Test Method). We present the best possible bound in the case of 0-1 valued loss fun...
Wojciech Jaworski
ADCM
2007
114views more  ADCM 2007»
9 years 2 months ago
Convergence analysis of online algorithms
In this paper, we are interested in the analysis of regularized online algorithms associated with reproducing kernel Hilbert spaces. General conditions on the loss function and st...
Yiming Ying
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