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    JMLR
    2006
    105views more  JMLR 2006»
    Some Theory for Generalized Boosting Algorithms
    13 years 11 months ago
    Some Theory for Generalized Boosting Algorithms
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    We give a review of various aspects of boosting, clarifying the issues through a few simple results, and relate our work and that of others to the minimax paradigm of statistics. ...
    Peter J. Bickel, Yaacov Ritov, Alon Zakai
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    JMLR
    2006
    125views more  JMLR 2006»
    Linear Programming Relaxations and Belief Propagation - An Empirical Study
    13 years 11 months ago
    Linear Programming Relaxations and Belief Propagation - An Empirical Study
    Download  jmlr.csail.mit.edu
    The problem of finding the most probable (MAP) configuration in graphical models comes up in a wide range of applications. In a general graphical model this problem is NP hard, bu...
    Chen Yanover, Talya Meltzer, Yair Weiss
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    JMLR
    2006
    118views more  JMLR 2006»
    Learning Factor Graphs in Polynomial Time and Sample Complexity
    13 years 11 months ago
    Learning Factor Graphs in Polynomial Time and Sample Complexity
    Download  jmlr.csail.mit.edu
    We study the computational and sample complexity of parameter and structure learning in graphical models. Our main result shows that the class of factor graphs with bounded degree...
    Pieter Abbeel, Daphne Koller, Andrew Y. Ng
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    JMLR
    2006
    124views more  JMLR 2006»
    Fast SDP Relaxations of Graph Cut Clustering, Transduction, and Other Combinatorial Problem
    13 years 11 months ago
    Fast SDP Relaxations of Graph Cut Clustering, Transduction, and Other Combinatorial Problem
    Download  ppw.kuleuven.be
    The rise of convex programming has changed the face of many research fields in recent years, machine learning being one of the ones that benefitted the most. A very recent develop...
    Tijl De Bie, Nello Cristianini
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    JMLR
    2006
    105views more  JMLR 2006»
    Linear State-Space Models for Blind Source Separation
    13 years 11 months ago
    Linear State-Space Models for Blind Source Separation
    Download  jmlr.csail.mit.edu
    We apply a type of generative modelling to the problem of blind source separation in which prior knowledge about the latent source signals, such as time-varying auto-correlation a...
    Rasmus Kongsgaard Olsson, Lars Kai Hansen
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    JMLR
    2006
    124views more  JMLR 2006»
    A Direct Method for Building Sparse Kernel Learning Algorithms
    13 years 11 months ago
    A Direct Method for Building Sparse Kernel Learning Algorithms
    Download  www.kyb.mpg.de
    Many kernel learning algorithms, including support vector machines, result in a kernel machine, such as a kernel classifier, whose key component is a weight vector in a feature sp...
    Mingrui Wu, Bernhard Schölkopf, Gökhan H...
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    JMLR
    2006
    117views more  JMLR 2006»
    On the Complexity of Learning Lexicographic Strategies
    13 years 11 months ago
    On the Complexity of Learning Lexicographic Strategies
    Download  www.ruhr-uni-bochum.de
    Fast and frugal heuristics are well studied models of bounded rationality. Psychological research has proposed the take-the-best heuristic as a successful strategy in decision mak...
    Michael Schmitt, Laura Martignon
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    JMLR
    2006
    97views more  JMLR 2006»
    Distance Patterns in Structural Similarity
    13 years 11 months ago
    Distance Patterns in Structural Similarity
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    Similarity of edge labeled graphs is considered in the sense of minimum squared distance between corresponding values. Vertex correspondences are established by isomorphisms if bo...
    Thomas Kämpke
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    JMLR
    2006
    82views more  JMLR 2006»
    Estimating the "Wrong" Graphical Model: Benefits in the Computation-Limited Setting
    13 years 11 months ago
    Estimating the "Wrong" Graphical Model: Benefits in the Computation-Limited Setting
    Download  www.cs.brown.edu
    Martin J. Wainwright
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    JMLR
    2006
    175views more  JMLR 2006»
    Learning Sparse Representations by Non-Negative Matrix Factorization and Sequential Cone Programming
    13 years 11 months ago
    Learning Sparse Representations by Non-Negative Matrix Factorization and Sequential Cone Programming
    Download  jmlr.csail.mit.edu
    We exploit the biconvex nature of the Euclidean non-negative matrix factorization (NMF) optimization problem to derive optimization schemes based on sequential quadratic and secon...
    Matthias Heiler, Christoph Schnörr
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