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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

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

14 2 3
zenecture/neuroflow

Artificial Neural Networks for Scala

Scala (JVM): 2.12

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

95 6 2
transcendent-ai-labs/dynaml

Scala Library/REPL for Machine Learning Research

Scala (JVM): 2.11

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

161 45 4

1 (current)

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