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    NIPS
    2003
    88views Information Technology» more  NIPS 2003»
    A Probabilistic Model of Auditory Space Representation in the Barn Owl
    13 years 6 months ago
    A Probabilistic Model of Auditory Space Representation in the Barn Owl
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    The barn owl is a nocturnal hunter, capable of capturing prey using auditory information alone [1]. The neural basis for this localization behavior is the existence of auditory ne...
    Brian J. Fischer, Charles H. Anderson
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    NIPS
    2003
    222views Information Technology» more  NIPS 2003»
    Mutual Boosting for Contextual Inference
    13 years 6 months ago
    Mutual Boosting for Contextual Inference
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    Mutual Boosting is a method aimed at incorporating contextual information to augment object detection. When multiple detectors of objects and parts are trained in parallel using A...
    Michael Fink 0002, Pietro Perona
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    NIPS
    2003
    196views Information Technology» more  NIPS 2003»
    Approximate Policy Iteration with a Policy Language Bias
    13 years 6 months ago
    Approximate Policy Iteration with a Policy Language Bias
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    We study an approach to policy selection for large relational Markov Decision Processes (MDPs). We consider a variant of approximate policy iteration (API) that replaces the usual...
    Alan Fern, Sung Wook Yoon, Robert Givan
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    NIPS
    2003
    151views Information Technology» more  NIPS 2003»
    An Autonomous Robotic System for Mapping Abandoned Mines
    13 years 6 months ago
    An Autonomous Robotic System for Mapping Abandoned Mines
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    David I. Ferguson, Aaron Morris, Dirk Hähnel,...
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    NIPS
    2003
    127views Information Technology» more  NIPS 2003»
    Fast Algorithms for Large-State-Space HMMs with Applications to Web Usage Analysis
    13 years 6 months ago
    Fast Algorithms for Large-State-Space HMMs with Applications to Web Usage Analysis
    Download  www.cs.cornell.edu
    In applying Hidden Markov Models to the analysis of massive data streams, it is often necessary to use an artificially reduced set of states; this is due in large part to the fac...
    Pedro F. Felzenszwalb, Daniel P. Huttenlocher, Jon...
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    NIPS
    2003
    136views Information Technology» more  NIPS 2003»
    An Improved Scheme for Detection and Labelling in Johansson Displays
    13 years 6 months ago
    An Improved Scheme for Detection and Labelling in Johansson Displays
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    Consider a number of moving points, where each point is attached to a joint of the human body and projected onto an image plane. Johannson showed that humans can effortlessly det...
    Claudio Fanti, Marzia Polito, Pietro Perona
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    NIPS
    2003
    133views Information Technology» more  NIPS 2003»
    Prediction on Spike Data Using Kernel Algorithms
    13 years 6 months ago
    Prediction on Spike Data Using Kernel Algorithms
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    We report and compare the performance of different learning algorithms based on data from cortical recordings. The task is to predict the orientation of visual stimuli from the ac...
    Jan Eichhorn, Andreas S. Tolias, Alexander Zien, M...
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    NIPS
    2003
    107views Information Technology» more  NIPS 2003»
    Increase Information Transfer Rates in BCI by CSP Extension to Multi-class
    13 years 6 months ago
    Increase Information Transfer Rates in BCI by CSP Extension to Multi-class
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    Brain-Computer Interfaces (BCI) are an interesting emerging technology that is driven by the motivation to develop an effective communication interface translating human intention...
    Guido Dornhege, Benjamin Blankertz, Gabriel Curio,...
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    NIPS
    2003
    167views Information Technology» more  NIPS 2003»
    When Does Non-Negative Matrix Factorization Give a Correct Decomposition into Parts?
    13 years 6 months ago
    When Does Non-Negative Matrix Factorization Give a Correct Decomposition into Parts?
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    We interpret non-negative matrix factorization geometrically, as the problem of finding a simplicial cone which contains a cloud of data points and which is contained in the posi...
    David L. Donoho, Victoria Stodden
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    NIPS
    2003
    123views Information Technology» more  NIPS 2003»
    Error Bounds for Transductive Learning via Compression and Clustering
    13 years 6 months ago
    Error Bounds for Transductive Learning via Compression and Clustering
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    This paper is concerned with transductive learning. Although transduction appears to be an easier task than induction, there have not been many provably useful algorithms and boun...
    Philip Derbeko, Ran El-Yaniv, Ron Meir
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