TY - GEN
T1 - Kernel Multi Label Vector Optimization (kMLVO)
T2 - 7th International Conference on Learning and Intelligent Optimization, LION 7
AU - Liberman, Gilad
AU - Vider-Shalit, Tal
AU - Louzoun, Yoram
PY - 2013
Y1 - 2013
N2 - We here propose the kMLVO (kernel Multi-Label Vector Optimization) framework designed to handle the common case in binary classification problems, where the observations, at least in part, are not given as an explicit class label, but rather as several scores which relate to the binary classification. Rather than handling each of the scores and the labeling data as separate problems, the kMLVO framework seeks a classifier which will satisfy all the corresponding constraints simultaneously. The framework can naturally handle problems where each of the scores is related differently to the classifying problem, optimizing both the classification, the regressions and the transformations into the different scores. Results from simulations and a protein docking problem in immunology are discussed, and the suggested method is shown to outperform both the corresponding SVM and SVR.
AB - We here propose the kMLVO (kernel Multi-Label Vector Optimization) framework designed to handle the common case in binary classification problems, where the observations, at least in part, are not given as an explicit class label, but rather as several scores which relate to the binary classification. Rather than handling each of the scores and the labeling data as separate problems, the kMLVO framework seeks a classifier which will satisfy all the corresponding constraints simultaneously. The framework can naturally handle problems where each of the scores is related differently to the classifying problem, optimizing both the classification, the regressions and the transformations into the different scores. Results from simulations and a protein docking problem in immunology are discussed, and the suggested method is shown to outperform both the corresponding SVM and SVR.
UR - http://www.scopus.com/inward/record.url?scp=84890932296&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-44973-4_15
DO - 10.1007/978-3-642-44973-4_15
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AN - SCOPUS:84890932296
SN - 9783642449727
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 131
EP - 137
BT - Learning and Intelligent Optimization - 7th International Conference, LION 7, Revised Selected Papers
Y2 - 7 January 2013 through 11 January 2013
ER -