svm VS svm-simple

Compare svm vs svm-simple and see what are their differences.

svm

A support vector machine implemented in Haskell. (by andrewdougherty)

svm-simple

Simplified interface to bindings-svm (by aleator)
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svm svm-simple
- -
33 6
- -
0.0 0.0
over 13 years ago over 7 years ago
Haskell Haskell
GNU General Public License v3.0 only BSD 3-clause "New" or "Revised" License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

svm

Posts with mentions or reviews of svm. We have used some of these posts to build our list of alternatives and similar projects.

We haven't tracked posts mentioning svm yet.
Tracking mentions began in Dec 2020.

svm-simple

Posts with mentions or reviews of svm-simple. We have used some of these posts to build our list of alternatives and similar projects.

We haven't tracked posts mentioning svm-simple yet.
Tracking mentions began in Dec 2020.

What are some alternatives?

When comparing svm and svm-simple you can also consider the following projects:

nn - A tiny neural network 🧠

hnn - haskell neural network library

SimpleEA - A simple evolutionary algorithm framework for Haskell

heukarya - genetic programming in haskell

HSGEP - Haskell Gene Expression Programming Library

tensor-safe - A Haskell framework to define valid deep learning models and export them to other frameworks like TensorFlow JS or Keras.

csp - Constraint satisfaction problem (CSP) solvers for Haskell

moo - Genetic algorithm library for Haskell. Binary and continuous (real-coded) GAs. Binary GAs: binary and Gray encoding; point mutation; one-point, two-point, and uniform crossover. Continuous GAs: Gaussian mutation; BLX-α, UNDX, and SBX crossover. Selection operators: roulette, tournament, and stochastic universal sampling (SUS); with optional niching, ranking, and scaling. Replacement strategies: generational with elitism and steady state. Constrained optimization: random constrained initialization, death penalty, constrained selection without a penalty function. Multi-objective optimization: NSGA-II and constrained NSGA-II.

neet - Neuroevolution of Augmented Topologies (NEAT) -- in Haskell