Fast Local Support Vector Machines for Large Datasets View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2009

AUTHORS

Nicola Segata , Enrico Blanzieri

ABSTRACT

Local SVM is a classification approach that combines instance-based learning and statistical machine learning. It builds an SVM on the feature space neighborhood of the query point in the training set and uses it to predict its class. There is both empirical and theoretical evidence that Local SVM can improve over SVM and kNN in terms of classification accuracy, but the computational cost of the method permits the application only on small datasets. Here we propose FastLSVM, a classifier based on Local SVM that decreases the number of SVMs that must be built in order to be suitable for large datasets. FastLSVM precomputes a set of local SVMs in the training set and assigns to each model all the points lying in the central neighborhood of the k points on which it is trained. The prediction is performed applying to the query point the model corresponding to its nearest neighbor in the training set. The empirical evaluation we provide points out that FastLSVM is a good approximation of Local SVM and its computational performances on big datasets (a large artificial problem with 100000 samples and a very large real problem with more than 500000 samples) dramatically ameliorate performances of SVM and its fast existing approximations improving also the generalization accuracies. More... »

PAGES

295-310

References to SciGraph publications

  • 1995-09. Support-vector networks in MACHINE LEARNING
  • 2005-11. Making SVMs Scalable to Large Data Sets using Hierarchical Cluster Indexing in DATA MINING AND KNOWLEDGE DISCOVERY
  • 1999-06. Least Squares Support Vector Machine Classifiers in NEURAL PROCESSING LETTERS
  • 2000. The Nature of Statistical Learning Theory in NONE
  • Book

    TITLE

    Machine Learning and Data Mining in Pattern Recognition

    ISBN

    978-3-642-03069-7
    978-3-642-03070-3

    Author Affiliations

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-642-03070-3_22

    DOI

    http://dx.doi.org/10.1007/978-3-642-03070-3_22

    DIMENSIONS

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