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    NIPS
    2003
    144views Information Technology» more  NIPS 2003»
    Margin Maximizing Loss Functions
    13 years 5 months ago
    Margin Maximizing Loss Functions
    Download  www.stat.lsa.umich.edu
    Margin maximizing properties play an important role in the analysis of classi£cation models, such as boosting and support vector machines. Margin maximization is theoretically in...
    Saharon Rosset, Ji Zhu, Trevor Hastie
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    SDM
    2004
    SIAM
    211views Data Mining» more  SDM 2004»
    Using Support Vector Machines for Classifying Large Sets of Multi-Represented Objects
    13 years 5 months ago
    Using Support Vector Machines for Classifying Large Sets of Multi-Represented Objects
    Download  www.dbs.informatik.uni-muenchen.de
    Databases are a key technology for molecular biology which is a very data intensive discipline. Since molecular biological databases are rather heterogeneous, unification and data...
    Hans-Peter Kriegel, Peer Kröger, Alexey Pryak...
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    ICMLA
    2003
    169views Machine Learning» more  ICMLA 2003»
    Robust Support Vector Machines for Anomaly Detection in Computer Security
    13 years 5 months ago
    Robust Support Vector Machines for Anomaly Detection in Computer Security
    Download  www.cs.ucdavis.edu
    — Using the 1998 DARPA BSM data set collected at MIT’s Lincoln Labs to study intrusion detection systems, the performance of robust support vector machines (RVSMs) was compared...
    Wenjie Hu, Yihua Liao, V. Rao Vemuri
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    NIPS
    2001
    149views Information Technology» more  NIPS 2001»
    Online Learning with Kernels
    13 years 5 months ago
    Online Learning with Kernels
    Download  eprints.pascal-network.org
    Abstract--Kernel-based algorithms such as support vector machines have achieved considerable success in various problems in batch setting, where all of the training data is availab...
    Jyrki Kivinen, Alex J. Smola, Robert C. Williamson
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    NIPS
    2001
    119views Information Technology» more  NIPS 2001»
    A Parallel Mixture of SVMs for Very Large Scale Problems
    13 years 5 months ago
    A Parallel Mixture of SVMs for Very Large Scale Problems
    Download  ronan.collobert.com
    Support Vector Machines (SVMs) are currently the state-of-the-art models for many classication problems but they suer from the complexity of their training algorithm which is at l...
    Ronan Collobert, Samy Bengio, Yoshua Bengio
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    FLAIRS
    2003
    150views Artificial Intelligence» more  FLAIRS 2003»
    Optimizing F-Measure with Support Vector Machines
    13 years 5 months ago
    Optimizing F-Measure with Support Vector Machines
    Download  www.aaai.org
    Support vector machines (SVMs) are regularly used for classification of unbalanced data by weighting more heavily the error contribution from the rare class. This heuristic techn...
    David R. Musicant, Vipin Kumar, Aysel Ozgur
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    NAACL
    2004
    112views Computational Linguistics» more  NAACL 2004»
    Shallow Semantic Parsing using Support Vector Machines
    13 years 5 months ago
    Shallow Semantic Parsing using Support Vector Machines
    Download  www.stanford.edu
    In this paper, we propose a machine learning algorithm for shallow semantic parsing, extending the work of Gildea and Jurafsky (2002), Surdeanu et al. (2003) and others. Our algor...
    Sameer Pradhan, Wayne Ward, Kadri Hacioglu, James ...
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    LWA
    2004
    104views Software Engineering» more  LWA 2004»
    A Simple Method For Estimating Conditional Probabilities For SVMs
    13 years 5 months ago
    A Simple Method For Estimating Conditional Probabilities For SVMs
    Download  www.stefan-rueping.de
    Support Vector Machines (SVMs) have become a popular learning algorithm, in particular for large, high-dimensional classification problems. SVMs have been shown to give most accur...
    Stefan Rüping
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    FLAIRS
    2001
    133views Artificial Intelligence» more  FLAIRS 2001»
    Improvement of Nearest-Neighbor Classifiers via Support Vector Machines
    13 years 5 months ago
    Improvement of Nearest-Neighbor Classifiers via Support Vector Machines
    Download  www.aaai.org
    Theoretically well-founded, Support Vector Machines (SVM)are well-knownto be suited for efficiently solving classification problems. Althoughimprovedgeneralization is the maingoal...
    Marc Sebban, Richard Nock
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    ESANN
    2001
    103views Neural Networks» more  ESANN 2001»
    Intelligent hardware for identification and control of non-linear systems with SVM
    13 years 5 months ago
    Intelligent hardware for identification and control of non-linear systems with SVM
    Download  www.dice.ucl.ac.be
    Support Vector Machines are gaining more and more acceptance thanks to their success in many real
    Andrea Boni, Fabio Bardi
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