Dear /farhan,

There are many classifiers algorithm in "weka" toolbox! it may help you.

Best regard,

Hamid 

Dear Farhan,

The best way is to download a chemometrics toolbox (google it).

Regards.

Ludovic.

check JOONE, or SNNS.

For Neural Networks, as far as I know, Matlab software itself has such a toolbox

RandomForest:http://www.stat.berkeley.edu/~breiman/RandomForests/

ELM:http://www.extreme-learning-machines.org/

The above machine learning algorithm toolboxes are what I have used in my own research.

For Python you can use scikit learn. It is a large collection of different machine learning algorithms that are easy to use:

http://scikit-learn.org/stable/

Dear /farhan,

There are many classifiers algorithm in "weka" toolbox! it may help you.

Best regard,

Hamid 

Thanks all for your response.

@Hamid Sami Does weka have any license limitations ? I have about 40k features and RapidMiner gives message that it cannot use than more than 1GB RAM in free license. 

Border pairs method and bipropagation are two my new very powerful algorithms. Much better than backpropagation. If something is unclear I will explain with pleasure. Please cite me.

Article Border Pairs Method—constructive MLP learning classification algorithm

Chapter Advances in Machine Learning Research

You can certainly use WEKA Machine Learning tool for not only classification using classifiers like NN, LIBLINEAR, SVM, Naive Bayes and many others but also one can use it for clustering using different clustering methods, feature dimension reduction using different techniques like PCA, filtering and lots more. It is based on JAVA so you can use across different OS. You can download it for free and see their work in the following link:

http://www.cs.waikato.ac.nz/ml/weka/

Apart form Weka you can use Matlab for NN, SVM etc. But Weka has much more collection.

Hope this will help you.

Regards,

Chayan Halder