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liquidsvm/liquidsvm

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.

Scala (JVM): 2.11

python apache-spark c-plus-plus quantile-regression r machine-learning regression octave r-package svm matlab classification rstats ml expectile-regression

14 2 3
zenecture/neuroflow

Artificial Neural Networks for Scala

Scala (JVM): 2.12

clustering data-artistry artificial-neural-networks regression pattern-recognition scala classification data-science

95 6 2
transcendent-ai-labs/dynaml

Scala Library/REPL for Machine Learning Research

Scala (JVM): 2.11

committee-models scala-library gaussian-processes tensorflow machine-learning regression machine-learning-api scala repl classification kernel-methods machine-learning-algorithms

161 45 4

1 (current)

Scaladex
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