Neural networks vs support vector machines: are the second definitely superior?
Many authors of papers I read affirm SVMs is superior technique to face their regression/classification problem, aware that they couldn't get similar results through NNs. Often the comparison states that SVMs, instead of NNs, Have a strong founding theory Reach the global optimum due to quadratic programming Have no issue for choosing a proper number of parameters Are less prone to overfitting Nee
GTSVM
Adult and covertype-1 are both binary datasets. The multiclass MNIST and TIMIT datasets are included to permit you to experiment with multiclass classification. The adult archives contain .dat files (in SVM-Light format) in addition to .mat files, but the rest only include .mat files. The adult and MNIST datasets were downloaded from Léon Bottou's LaSVM web page. The covertype-1 dataset is origina
LIBLINEAR -- A Library for Large Linear Classification
Machine Learning Group at National Taiwan University Contributors Version 2.47 released on July 9, 2023. We fix some minor bugs. Version 2.43 released on February 25, 2021. Installing the Python interface through PyPI is supported > pip install -U liblinear-official The python directory is re-organized so >>> from liblinear.liblinearutil import * instead of >>> from liblinearutil import * should b
LIBSVM -- A Library for Support Vector Machines
Chih-Chung Chang and Chih-Jen Lin Version 3.35 released on September 1, 2024. We fix some minor bugs. Version 3.31 released on February 28, 2023. Probabilistic outputs for one-class SVM are now supported. Version 3.25 released on April 14, 2021. Installing the Python interface through PyPI is supported > pip install -U libsvm-official The python directory is re-organized so >>> from libsvm.svmutil
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