TY - GEN
T1 - An overview of learning algorithms and inference techniques on restricted boltzmann machines (rbms)
AU - Merindasari, Esti
AU - Widyanto, M. Rahmat
AU - Basaruddin, T.
N1 - Publisher Copyright:
© 2019 Association for Computing Machinery.
PY - 2019/1/10
Y1 - 2019/1/10
N2 - Restricted Boltzmann Machines (RBMs) is one of machine learning's methods which within past decades, the development of RBMs has quite increase. Researches of RBMs focused on theories and applications of RBMs. The application of RBMs has proofed that RBMs good at finishing many tasks, such as feature extraction method, document modeling, representation learning, classification and others. The RBMs' theories also have great movements, such as the development of the learning algorithm and inference techniques of RBMs. The key factors making the RBM success on finishing task are the learning algorithm and inference techniques. They motivated the development of inference techniques which successfully improved the deep neural network (DNN) performance. The aim of this research is reviewing the various types of RBMs as the application side, and the development of learning algorithm and inference techniques as theoretical side. Hopefully, it could motivate more development on the RBMs in order to contribute on overcoming implementation tasks especially on image processing tasks.
AB - Restricted Boltzmann Machines (RBMs) is one of machine learning's methods which within past decades, the development of RBMs has quite increase. Researches of RBMs focused on theories and applications of RBMs. The application of RBMs has proofed that RBMs good at finishing many tasks, such as feature extraction method, document modeling, representation learning, classification and others. The RBMs' theories also have great movements, such as the development of the learning algorithm and inference techniques of RBMs. The key factors making the RBM success on finishing task are the learning algorithm and inference techniques. They motivated the development of inference techniques which successfully improved the deep neural network (DNN) performance. The aim of this research is reviewing the various types of RBMs as the application side, and the development of learning algorithm and inference techniques as theoretical side. Hopefully, it could motivate more development on the RBMs in order to contribute on overcoming implementation tasks especially on image processing tasks.
KW - Inference
KW - Learning algorithm
KW - Restricted boltzmann machine
UR - http://www.scopus.com/inward/record.url?scp=85063583894&partnerID=8YFLogxK
U2 - 10.1145/3305160.3305194
DO - 10.1145/3305160.3305194
M3 - Conference contribution
AN - SCOPUS:85063583894
T3 - ACM International Conference Proceeding Series
SP - 16
EP - 20
BT - Proceedings of the 2019 2nd International Conference on Software Engineering and Information Management, ICSIM 2019 - Workshop 2019 2nd International Conference on Big Data and Smart Computing, ICBDSC 2019
PB - Association for Computing Machinery
T2 - 2nd International Conference on Software Engineering and Information Management, ICSIM 2019 - and its Workshop 2019 2nd International Conference on Big Data and Smart Computing, ICBDSC 2019
Y2 - 10 January 2019 through 13 January 2019
ER -