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    IJCNN
    2006
    IEEE
    109views Neural Networks» more  IJCNN 2006»
    On derivation of stagewise second-order backpropagation by invariant imbedding for multi-stage neural-network learning
    15 years 10 months ago
    On derivation of stagewise second-order backpropagation by invariant imbedding for multi-stage neural-network learning
    Download  www.ieor.berkeley.edu
    — We present a simple, intuitive argument based on “invariant imbedding” in the spirit of dynamic programming to derive a stagewise second-order backpropagation (BP) algorith...
    Eiji Mizutani, Stuart Dreyfus
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    IJCNN
    2006
    IEEE
    101views Neural Networks» more  IJCNN 2006»
    A Variational EM Approach to Predicting Uncertainty in Supervised Learning
    15 years 10 months ago
    A Variational EM Approach to Predicting Uncertainty in Supervised Learning
    Download  www.cis.hut.fi
    — In many applications of supervised learning, the conditional average of the target variables is not sufficient for prediction. The dependencies between the explanatory variabl...
    Markus Harva
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    BMCBI
    2008
    136views more  BMCBI 2008»
    A comparison of machine learning algorithms for chemical toxicity classification using a simulated multi-scale data model
    15 years 4 months ago
    A comparison of machine learning algorithms for chemical toxicity classification using a simulated multi-scale data model
    Download  www.biomedcentral.com
    Background: Bioactivity profiling using high-throughput in vitro assays can reduce the cost and time required for toxicological screening of environmental chemicals and can also r...
    Richard Judson, Fathi Elloumi, R. Woodrow Setzer, ...
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    ICANN
    2009
    Springer
    141views Neural Networks» more  ICANN 2009»
    Adaptive Ensemble Models of Extreme Learning Machines for Time Series Prediction
    15 years 10 months ago
    Adaptive Ensemble Models of Extreme Learning Machines for Time Series Prediction
    Download  www.icsi.berkeley.edu
    Abstract. In this paper, we investigate the application of adaptive ensemble models of Extreme Learning Machines (ELMs) to the problem of one-step ahead prediction in (non)stationa...
    Mark van Heeswijk, Yoan Miche, Tiina Lindh-Knuutil...
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    IJCNN
    2006
    IEEE
    146views Neural Networks» more  IJCNN 2006»
    Learning the Kernel in Mahalanobis One-Class Support Vector Machines
    15 years 10 months ago
    Learning the Kernel in Mahalanobis One-Class Support Vector Machines
    Download  www.cse.ust.hk
    — In this paper, we show that one-class SVMs can also utilize data covariance in a robust manner to improve performance. Furthermore, by constraining the desired kernel function ...
    Ivor W. Tsang, James T. Kwok, Shutao Li
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