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    NIPS
    1998
    102views Information Technology» more  NIPS 1998»
    Analyzing and Visualizing Single-Trial Event-Related Potentials
    13 years 11 months ago
    Analyzing and Visualizing Single-Trial Event-Related Potentials
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    Tzyy-Ping Jung, Scott Makeig, Marissa Westerfield,...
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    NIPS
    1998
    164views Information Technology» more  NIPS 1998»
    Finite-Sample Convergence Rates for Q-Learning and Indirect Algorithms
    13 years 11 months ago
    Finite-Sample Convergence Rates for Q-Learning and Indirect Algorithms
    Download  www.cis.upenn.edu
    In this paper, we address two issues of long-standing interest in the reinforcement learning literature. First, what kinds of performance guarantees can be made for Q-learning aft...
    Michael J. Kearns, Satinder P. Singh
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    NIPS
    1998
    121views Information Technology» more  NIPS 1998»
    Inference in Multilayer Networks via Large Deviation Bounds
    13 years 11 months ago
    Inference in Multilayer Networks via Large Deviation Bounds
    Download  www.cis.upenn.edu
    We study probabilistic inference in large, layered Bayesian networks represented as directed acyclic graphs. We show that the intractability of exact inference in such networks do...
    Michael J. Kearns, Lawrence K. Saul
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    NIPS
    1998
    95views Information Technology» more  NIPS 1998»
    Maximum Conditional Likelihood via Bound Maximization and the CEM Algorithm
    13 years 11 months ago
    Maximum Conditional Likelihood via Bound Maximization and the CEM Algorithm
    Download  www.cs.columbia.edu
    We present the CEM (Conditional Expectation Maximization) algorithm as an extension of the EM (Expectation Maximization) algorithm to conditional density estimation under missing ...
    Tony Jebara, Alex Pentland
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    NIPS
    1998
    74views Information Technology» more  NIPS 1998»
    Restructuring Sparse High Dimensional Data for Effective Retrieval
    13 years 11 months ago
    Restructuring Sparse High Dimensional Data for Effective Retrieval
    Download  www.cs.rutgers.edu
    The task in text retrieval is to find the subset of a collection of documents relevant to a user's information request, usually expressed as a set of words. Classically, docu...
    Charles Lee Isbell Jr., Paul A. Viola
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    NIPS
    1998
    147views Information Technology» more  NIPS 1998»
    Convergence of the Wake-Sleep Algorithm
    13 years 11 months ago
    Convergence of the Wake-Sleep Algorithm
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    The W-S (Wake-Sleep) algorithm is a simple learning rule for the models with hidden variables. It is shown that this algorithm can be applied to a factor analysis model which is a...
    Shiro Ikeda, Shun-ichi Amari, Hiroyuki Nakahara
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    NIPS
    1998
    138views Information Technology» more  NIPS 1998»
    Call-Based Fraud Detection in Mobile Communication Networks Using a Hierarchical Regime-Switching Model
    13 years 11 months ago
    Call-Based Fraud Detection in Mobile Communication Networks Using a Hierarchical Regime-Switching Model
    Download  lib.tkk.fi
    Fraud causes substantial losses to telecommunication carriers. Detection systems which automatically detect illegal use of the network can be used to alleviate the problem. Previo...
    Jaakko Hollmén, Volker Tresp
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    NIPS
    1998
    112views Information Technology» more  NIPS 1998»
    Learning from Dyadic Data
    13 years 11 months ago
    Learning from Dyadic Data
    Download  www.cs.berkeley.edu
    Dyadic data refers to a domain with two nite sets of objects in which observations are made for dyads, i.e., pairs with one element from either set. This type of data arises natur...
    Thomas Hofmann, Jan Puzicha, Michael I. Jordan
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    NIPS
    1998
    153views Information Technology» more  NIPS 1998»
    Outcomes of the Equivalence of Adaptive Ridge with Least Absolute Shrinkage
    13 years 11 months ago
    Outcomes of the Equivalence of Adaptive Ridge with Least Absolute Shrinkage
    Download  www.hds.utc.fr
    Adaptive Ridge is a special form of Ridge regression, balancing the quadratic penalization on each parameter of the model. It was shown to be equivalent to Lasso (least absolute s...
    Yves Grandvalet, Stéphane Canu
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    NIPS
    1998
    156views Information Technology» more  NIPS 1998»
    Learning Nonlinear Dynamical Systems Using an EM Algorithm
    13 years 11 months ago
    Learning Nonlinear Dynamical Systems Using an EM Algorithm
    Download  www.cs.nyu.edu
    The Expectation Maximization EM algorithm is an iterative procedure for maximum likelihood parameter estimation from data sets with missing or hidden variables 2 . It has been app...
    Zoubin Ghahramani, Sam T. Roweis
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