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LEMGA: Learning Models and Generic Introduction with Examples
 

Summary: LEMGA: Learning Models and Generic
Algorithms
Introduction with Examples
(Updated to v0.1)
Ling Li
Learning Systems Group, Caltech
January 22, 2003
What is Lemga?
Lemga is a C++ library consisting of
Learning models/frames: neural networks, decision stumps, bagging,
boosting, . . .
Generic optimization algorithms: gradient descent, conjugate-gradient,
. . .
Auxiliary parts: data set, . . .
Features
Uniform interface for learning models: initialize(), set train data(),
train(), > >, < <, create(), . . .
Flexible training methods: different optimization methods can be applied
to a same object
Easily extendable

  

Source: Abu-Mostafa, Yaser S. - Department of Mechanical Engineering & Computer Science Department, California Institute of Technology

 

Collections: Computer Technologies and Information Sciences