{"id":171,"date":"2017-02-13T17:58:39","date_gmt":"2017-02-13T17:58:39","guid":{"rendered":"http:\/\/sag.art.uniroma2.it\/kelp_wordpress\/?page_id=171"},"modified":"2017-04-06T15:52:53","modified_gmt":"2017-04-06T15:52:53","slug":"hello-linear-learning","status":"publish","type":"page","link":"http:\/\/www.kelp-ml.org\/?page_id=171","title":{"rendered":"Hello (linear) Learning!"},"content":{"rendered":"

Let’s start with a very simple classification example based on a linear version of the Passive Aggressive algorithm (LinearPassiveAggressiveClassification<\/a>). The full code of this example can be found in the GitHub<\/a> repository kelp-full<\/a>, in particular in the source file HelloLearning.java<\/a><\/em>.<\/p>\n

Dataset used here is the same of the svmlight<\/a> page; each example is only modified to be readable by KeLP<\/strong>. In fact, a single row in KeLP<\/strong>\u00a0must indicate what kind of vectors your are using, Sparse<\/a> or Dense<\/a>. In the svmlight<\/a>\u00a0dataset there are sparse vectors, so if you open the train.dat and test.dat files you can notice that each vector is enclosed in BeginVector (|BV|) and EndVector (|EV|) tags.<\/p>\n

The classification task consists in classifying an example with respect to the “+1” and “-1” classes. The dataset is thus composed by examples of such classes:<\/p>\n