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    » Learning to Rank Using an Ensemble of Lambda-Gradient Models
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    ESWA
    2008
    223views more  ESWA 2008»
    Credit risk assessment with a multistage neural network ensemble learning approach
    13 years 7 months ago
    Credit risk assessment with a multistage neural network ensemble learning approach
    Download  madis1.iss.ac.cn
    In this study, a multistage neural network ensemble learning model is proposed to evaluate credit risk at the measurement level. The proposed model consists of six stages. In the ...
    Lean Yu, Shouyang Wang, Kin Keung Lai
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    CIKM
    2008
    Springer
    119views Information Technology» more  CIKM 2008»
    Error-driven generalist+experts (edge): a multi-stage ensemble framework for text categorization
    13 years 9 months ago
    Error-driven generalist+experts (edge): a multi-stage ensemble framework for text categorization
    Download  www.personal.psu.edu
    We introduce a multi-stage ensemble framework, ErrorDriven Generalist+Expert or Edge, for improved classification on large-scale text categorization problems. Edge first trains a ...
    Jian Huang 0002, Omid Madani, C. Lee Giles
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    KDD
    2003
    ACM
    148views Data Mining» more  KDD 2003»
    Mining concept-drifting data streams using ensemble classifiers
    14 years 8 months ago
    Mining concept-drifting data streams using ensemble classifiers
    Download  wis.cs.ucla.edu
    Recently, mining data streams with concept drifts for actionable insights has become an important and challenging task for a wide range of applications including credit card fraud...
    Haixun Wang, Wei Fan, Philip S. Yu, Jiawei Han
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    NIPS
    2008
    159views Information Technology» more  NIPS 2008»
    Structured ranking learning using cumulative distribution networks
    13 years 9 months ago
    Structured ranking learning using cumulative distribution networks
    Download  research.microsoft.com
    Ranking is at the heart of many information retrieval applications. Unlike standard regression or classification in which we predict outputs independently, in ranking we are inter...
    Jim C. Huang, Brendan J. Frey
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    IJCNN
    2006
    IEEE
    73views Neural Networks» more  IJCNN 2006»
    Statistical Mechanics of Online Learning for Ensemble Teachers
    14 years 1 months ago
    Statistical Mechanics of Online Learning for Ensemble Teachers
    Download  www2.ipcku.kansai-u.ac.jp
    — We analyze the generalization performance of a student in a model composed of linear perceptrons: a true teacher, ensemble teachers, and the student. Calculating the generaliza...
    Seiji Miyoshi, Masato Okada
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