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.
- matlab
- apache-spark
- c-plus-plus
- octave
- rstats
- python
- svm
- machine-learning
- expectile-regression
- classification
- ml
- r-package
- quantile-regression
- regression
- r
Scala versions:
2.11
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"
ivy"de.unistuttgart.isa::liquidSVM-spark:0.6.0"
//> using dep "de.unistuttgart.isa::liquidSVM-spark:0.6.0"
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'
