Building/Running a Streaming Weka Text Classifer in Java?

To my best knowledge you need to retrain weka classifier when a new training sample arrives. I am not aware of an online classification algorithm in Wekka.

To my best knowledge you need to retrain weka classifier when a new training sample arrives. I am not aware of an online classification algorithm in Wekka. Ps.

Weka is Java based, so you can use its libs in your application. Here is a good example: weka.wikispaces.com/Use+WEKA+in+your+Jav....

Thanks. I know that there is no way to add new training samples without retraining (though some classifier models are updatable). However classifying new messages, which are untagged (i.e.

A test set without tags). For the classifier we are using the NGram Tokenizer, Stemmer and IDF Transform. So we need to figure out how to do these steps before we can create a new instancebased on the text we would like to classify.

– NightWolf Aug 27 at 12:49 It is not very clear for me, what your problem is. As I understand you have a text processing pipeline in place for processing new messages. You know how wekka works and you can embedded it in your java application.So now, you are looking for a way to handle unforeseeable tokens in new messages.

Do I understand it correctly? – Skarab Aug 27 at 15:13.

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