Space: Apache Mahout (https://cwiki.apache.org/confluence/display/MAHOUT)
Page: Online Passive Aggressive 
(https://cwiki.apache.org/confluence/display/MAHOUT/Online+Passive+Aggressive)

Added by Hector Yee:
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Online Passive Aggressive

Implements 
[http://www.google.com/url?sa=t&source=web&cd=1&ved=0CCIQFjAA&url=http%3A%2F%2Fciteseer.ist.psu.edu%2Fviewdoc%2Fdownload%3Bjsessionid%3DF4743238B0EF35EB396A5ABFF1332021%3Fdoi%3D10.1.1.61.5120%26rep%3Drep1%26type%3Dpdf&rct=j&q=online%20passive%20aggressive&ei=elvWTa6jBcfHrQf8o52KBg&usg=AFQjCNGqNjaHyWgT4Z3QrK7hEqSTGM10YQ&sig2=-szWIrzBLoQ52jBER9-I0Q&cad=rja].

Use cases:

  When you have many classes that are linearly separable and want a fast online 
learner to get results quickly.

Pre-requisites:

  Data must be shuffled and normalized either between 0..1 or by mean and 
standard deviation.

Technical details:

  The training approach taken is to minimize the ranking loss of the correct 
label vs the incorrect ones. We define this loss as hinge(1 - correct label 
score + wrong label score) where wrong label score is the score of the highest 
scoring label that is not the correct label. The hinge function is hinge(x) = x 
if x > 0, 0 otherwise.

Parameters:

  There is only one - learningRate. You set it to a larger number to converge 
faster, or a smaller number to be more cautious. The normal way to use it is 
via cross validation. Good values are (0.1, 1.0, 10.0).

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