An efficient many-core implementation for semi-supervised support vector machines

TY - GEN

T1 - An efficient many-core implementation for semi-supervised support vector machines

AU - Gieseke, Fabian

N1 - Conference code: 1

PY - 2015

Y1 - 2015

N2 - The concept of semi-supervised support vector machines extends classical support vector machines to learning scenarios, where both labeled and unlabeled patterns are given. In recent years, such semi-supervised extensions have gained considerable attention due to their huge potential for real-world applications with only small amounts of labeled data. While being appealing from a practical point of view, semi-supervised support vector machines lead to a combinatorial optimization problem that is difficult to address. Many optimization approaches have been proposed that aim at tackling this task. However, the computational requirements can still be very high, especially in case large data sets are considered and many model parameters need to be tuned. A recent trend in the field of big data analytics is to make use of graphics processing units to speed up computationally intensive tasks. In this work, such a massively-parallel implementation is developed for semi-supervised support vector machines. The experimental evaluation, conducted on commodity hardware, shows that valuable speed-ups of up to two orders of magnitude can be achieved over a standard single-core CPU execution.

AB - The concept of semi-supervised support vector machines extends classical support vector machines to learning scenarios, where both labeled and unlabeled patterns are given. In recent years, such semi-supervised extensions have gained considerable attention due to their huge potential for real-world applications with only small amounts of labeled data. While being appealing from a practical point of view, semi-supervised support vector machines lead to a combinatorial optimization problem that is difficult to address. Many optimization approaches have been proposed that aim at tackling this task. However, the computational requirements can still be very high, especially in case large data sets are considered and many model parameters need to be tuned. A recent trend in the field of big data analytics is to make use of graphics processing units to speed up computationally intensive tasks. In this work, such a massively-parallel implementation is developed for semi-supervised support vector machines. The experimental evaluation, conducted on commodity hardware, shows that valuable speed-ups of up to two orders of magnitude can be achieved over a standard single-core CPU execution.

U2 - 10.1007/978-3-319-27926-8_13

DO - 10.1007/978-3-319-27926-8_13

M3 - Article in proceedings

SN - 978-3-319-27925-1

T3 - Lecture notes in computer science

SP - 145

EP - 157

BT - Machine Learning, Optimization, and Big Data

A2 - Pardalos, Panos

A2 - Pavone, Mario

A2 - Farinella, Giovanni Maria

A2 - Cutello, Vincenzo

PB - Springer

T2 - First International Workshop on Machine Learning, Optimization, and Big Data

Y2 - 21 July 2015 through 23 July 2015

ER -