Support vector machines (SVMs) and related kernel-based learning algorithms are a well-known class of machine learning algorithms, for non-parametric classification and regression. liquidSVM is an implementation of SVMs whose key features are: fully integrated hyper-parameter selection, extreme speed on both small and large data sets, full flexibility for experts, and inclusion of a variety of different learning scenarios: multi-class classification, ROC, and Neyman-Pearson learning, and least-squares, quantile, and expectile regression.

liquidSVM-spark 0.6.0


Group ID:
de.unistuttgart.isa
Artifact ID:
liquidSVM-spark_2.11
Version:
0.6.0
Release Date:
Aug 9, 2018
Licenses:
Files:

libraryDependencies += "de.unistuttgart.isa" %% "liquidSVM-spark" % "0.6.0"

Mill build tool

ivy"de.unistuttgart.isa::liquidSVM-spark:0.6.0"

Scala CLI

//> using lib de.unistuttgart.isa::liquidSVM-spark:0.6.0

Ammonite REPL

import $ivy.`de.unistuttgart.isa::liquidSVM-spark:0.6.0`

<dependency>
  <groupId>de.unistuttgart.isa</groupId>
  <artifactId>liquidSVM-spark_2.11</artifactId>
  <version>0.6.0</version>
</dependency>

compile group: 'de.unistuttgart.isa', name: 'liquidSVM-spark_2.11', version: '0.6.0'