TY - GEN
T1 - Speedy local search for semi-supervised regularized least-squares
AU - Gieseke, Fabian
AU - Kramer, Oliver
AU - Airola, Antti
AU - Pahikkala, Tapio
PY - 2011
Y1 - 2011
N2 - In real-world machine learning scenarios, labeled data is often rare while unlabeled data can be obtained easily. Semi-supervised approaches aim at improving the prediction performance by taking both the labeled as well as the unlabeled part of the data into account. In particular, semi-supervised support vector machines favor decision hyperplanes which lie in a "low-density area" induced by the unlabeled patterns (while still considering the labeled part of the data). The associated optimization problem, however, is of combinatorial nature and, hence, difficult to solve. In this work, we present an efficient implementation of a simple local search strategy that is based on matrix updates of the intermediate candidate solutions. Our experiments on both artificial and real-world data sets indicate that the approach can successfully incorporate unlabeled data in an efficient manner.
AB - In real-world machine learning scenarios, labeled data is often rare while unlabeled data can be obtained easily. Semi-supervised approaches aim at improving the prediction performance by taking both the labeled as well as the unlabeled part of the data into account. In particular, semi-supervised support vector machines favor decision hyperplanes which lie in a "low-density area" induced by the unlabeled patterns (while still considering the labeled part of the data). The associated optimization problem, however, is of combinatorial nature and, hence, difficult to solve. In this work, we present an efficient implementation of a simple local search strategy that is based on matrix updates of the intermediate candidate solutions. Our experiments on both artificial and real-world data sets indicate that the approach can successfully incorporate unlabeled data in an efficient manner.
UR - http://www.scopus.com/inward/record.url?scp=80053961956&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-24455-1_8
DO - 10.1007/978-3-642-24455-1_8
M3 - Article in proceedings
AN - SCOPUS:80053961956
SN - 978-3-642-24454-4
T3 - Lecture notes in computer science
SP - 87
EP - 98
BT - KI 2011: Advances in Artificial Intelligence
A2 - Bach, Joscha
A2 - Edelkamp, Stefan
T2 - 34th Annual German Conference on Artificial Intelligence, KI 2011, in Co-location with the 41st Annual Meeting of the Gesellschaft fur Informatik, INFORMATIK 2011 and the 9th German Conference on Multi-Agent System Technologies, MATES 2011
Y2 - 4 October 2011 through 7 October 2011
ER -