logo kernelLogisticRegression

This package contains a classifier that can be used to train a two-class kernel logistic regression model with the kernel functions that are available in WEKA. It optimises the negative log-likelihood with a quadratic penalty. Both, BFGS and conjugate gradient descent, are available as optimisation methods, but the former is normally faster. It is possible to use multiple threads, but the speed-up is generally very marginal when used with BFGS optimisation. With conjugate gradient descent optimisation, greater speed-ups can be achieved when using multiple threads. With the default kernel, the dot product kernel, this method produces results that are close to identical to those obtained using standard logistic regression in WEKA, provided a sufficiently large value for the parameter determining the size of the quadratic penalty is used in both cases.

homepage: weka.sourceforge.net/doc.packages/kernelLogisticRegression
fresh index:
last release: 5 years ago, first release: 5 years ago
packaging: jar
get this artifact from: central
see this artifact on: search.maven.org

How much is this artifact used as a dependency in other Maven artifacts in Central repository and GitHub:

© Jiri Pinkas 2015 - 2018. All rights reserved. Admin login To submit bugs / feature requests please use this github page
related: JavaVids | Top Java Blogs | Java školení | 4npm - npm search | monitored using: sitemonitoring
Apache and Apache Maven are trademarks of the Apache Software Foundation. The Central Repository is a service mark of Sonatype, Inc.