TY - GEN
T1 - Online knowledge-based support vector machines
AU - Kunapuli, Gautam
AU - Bennett, Kristin P.
AU - Shabbeer, Amina
AU - MacLin, Richard
AU - Shavlik, Jude
N1 - Copyright:
Copyright 2010 Elsevier B.V., All rights reserved.
PY - 2010
Y1 - 2010
N2 - Prior knowledge, in the form of simple advice rules, can greatly speed up convergence in learning algorithms. Online learning methods predict the label of the current point and then receive the correct label (and learn from that information). The goal of this work is to update the hypothesis taking into account not just the label feedback, but also the prior knowledge, in the form of soft polyhedral advice, so as to make increasingly accurate predictions on subsequent examples. Advice helps speed up and bias learning so that generalization can be obtained with less data. Our passive-aggressive approach updates the hypothesis using a hybrid loss that takes into account the margins of both the hypothesis and the advice on the current point. Encouraging computational results and loss bounds are provided.
AB - Prior knowledge, in the form of simple advice rules, can greatly speed up convergence in learning algorithms. Online learning methods predict the label of the current point and then receive the correct label (and learn from that information). The goal of this work is to update the hypothesis taking into account not just the label feedback, but also the prior knowledge, in the form of soft polyhedral advice, so as to make increasingly accurate predictions on subsequent examples. Advice helps speed up and bias learning so that generalization can be obtained with less data. Our passive-aggressive approach updates the hypothesis using a hybrid loss that takes into account the margins of both the hypothesis and the advice on the current point. Encouraging computational results and loss bounds are provided.
UR - http://www.scopus.com/inward/record.url?scp=78049413627&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78049413627&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-15883-4_10
DO - 10.1007/978-3-642-15883-4_10
M3 - Conference contribution
AN - SCOPUS:78049413627
SN - 364215882X
SN - 9783642158827
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 145
EP - 161
BT - Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2010, Proceedings
T2 - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2010
Y2 - 20 September 2010 through 24 September 2010
ER -