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    ML
    2007
    ACM
    131views Machine Learning» more  ML 2007»
    A primal-dual perspective of online learning algorithms
    13 years 11 months ago
    A primal-dual perspective of online learning algorithms
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    We describe a novel framework for the design and analysis of online learning algorithms based on the notion of duality in constrained optimization. We cast a sub-family of universa...
    Shai Shalev-Shwartz, Yoram Singer
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    ML
    2007
    ACM
    99views Machine Learning» more  ML 2007»
    Classifying under computational resource constraints: anytime classification using probabilistic estimators
    13 years 11 months ago
    Classifying under computational resource constraints: anytime classification using probabilistic estimators
    Download  www.csse.monash.edu
    Ying Yang, Geoffrey I. Webb, Kevin B. Korb, Kai Mi...
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    ML
    2007
    ACM
    106views Machine Learning» more  ML 2007»
    Surrogate maximization/minimization algorithms and extensions
    13 years 11 months ago
    Surrogate maximization/minimization algorithms and extensions
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    Abstract Surrogate maximization (or minimization) (SM) algorithms are a family of algorithms that can be regarded as a generalization of expectation-maximization (EM) algorithms. A...
    Zhihua Zhang, James T. Kwok, Dit-Yan Yeung
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    ML
    2007
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    192views Machine Learning» more  ML 2007»
    Annealing stochastic approximation Monte Carlo algorithm for neural network training
    13 years 11 months ago
    Annealing stochastic approximation Monte Carlo algorithm for neural network training
    Download  www.stat.tamu.edu
    We propose a general-purpose stochastic optimization algorithm, the so-called annealing stochastic approximation Monte Carlo (ASAMC) algorithm, for neural network training. ASAMC c...
    Faming Liang
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    ML
    2007
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    156views Machine Learning» more  ML 2007»
    Active learning for logistic regression: an evaluation
    13 years 11 months ago
    Active learning for logistic regression: an evaluation
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    Which active learning methods can we expect to yield good performance in learning binary and multi-category logistic regression classifiers? Addressing this question is a natural ...
    Andrew I. Schein, Lyle H. Ungar
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    ML
    2007
    ACM
    144views Machine Learning» more  ML 2007»
    Invariant kernel functions for pattern analysis and machine learning
    13 years 11 months ago
    Invariant kernel functions for pattern analysis and machine learning
    Download  aam.mathematik.uni-freiburg.de
    In many learning problems prior knowledge about pattern variations can be formalized and beneficially incorporated into the analysis system. The corresponding notion of invarianc...
    Bernard Haasdonk, Hans Burkhardt
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    ML
    2007
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    130views Machine Learning» more  ML 2007»
    A note on Platt's probabilistic outputs for support vector machines
    13 years 11 months ago
    A note on Platt's probabilistic outputs for support vector machines
    Download  www.csie.ntu.edu.tw
    Platt’s probabilistic outputs for Support Vector Machines (Platt, 2000) has been popular for applications that require posterior class probabilities. In this note, we propose an ...
    Hsuan-Tien Lin, Chih-Jen Lin, Ruby C. Weng
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    ML
    2007
    ACM
    113views Machine Learning» more  ML 2007»
    PAV and the ROC convex hull
    13 years 11 months ago
    PAV and the ROC convex hull
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    Classifier calibration is the process of converting classifier scores into reliable probability estimates. Recently, a calibration technique based on isotonic regression has gain...
    Tom Fawcett, Alexandru Niculescu-Mizil
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    ML
    2007
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    85views Machine Learning» more  ML 2007»
    Predictive Modelling of Heterogeneous Sequence Collections by Topographic Ordering of Histories
    13 years 11 months ago
    Predictive Modelling of Heterogeneous Sequence Collections by Topographic Ordering of Histories
    Download  www.cs.bham.ac.uk
    Ata Kabán
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    ML
    2007
    ACM
    153views Machine Learning» more  ML 2007»
    Multi-Class Learning by Smoothed Boosting
    13 years 11 months ago
    Multi-Class Learning by Smoothed Boosting
    Download  www.springerlink.com
    AdaBoost.OC has been shown to be an effective method in boosting “weak” binary classifiers for multi-class learning. It employs the Error-Correcting Output Code (ECOC) method ...
    Rong Jin, Jian Zhang 0003
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