Ensemble Classification Restricted Boltzmann Machines: A Deep Learning Based Classification Method

The aim of this letter is to propose a theory of deep restricted kernel machines offering new foundations for deep learning with kernel machines. From the viewpoint of deep learning, it is partially related to restricted Boltzmann machines, which are characterized by visible and hidden units in a bipartite graph without hidden-to-hidden connections and deep learning extensions as deep belief networks and deep Boltzmann machines. From the viewpoint of kernel machines, it includes least squares support vector machines for classification and regression, kernel principal component analysis (PCA), matrix singular value decomposition, and Parzen-type models. A key element is to first characterize these kernel machines in terms of so-called conjugate feature duality, yielding a representation with visible and hidden units. It is shown how this is related to the energy form in restricted Boltzmann machines, with continuous variables in a nonprobabilistic setting. In this new framework of so-called restricted kernel machine (RKM) representations, the dual variables correspond to hidden features. Deep RKM are obtained by coupling the RKMs. The method is illustrated for deep RKM, consisting of three levels with a least squares support vector machine regression level and two kernel PCA levels. In its primal form also deep feedforward neural networks can be trained within this framework.

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A Deep Learning Scheme for Motor Imagery Classification based on Restricted Boltzmann Machines

IEEE Transactions on Neural Systems and Rehabilitation Engineering ◽

10.1109/tnsre.2016.2601240 ◽

2017 ◽

Vol 25 (6) ◽

pp. 566-576 ◽

Cited By ~ 129

Author(s):

Na Lu ◽

Tengfei Li ◽

Xiaodong Ren ◽

Hongyu Miao

Keyword(s):

Deep Learning ◽

Motor Imagery ◽

Restricted Boltzmann Machines ◽

Boltzmann Machines ◽

Learning Scheme

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Protein Function Prediction Using Deep Restricted Boltzmann Machines

BioMed Research International ◽

10.1155/2017/1729301 ◽

2017 ◽

Vol 2017 ◽

pp. 1-9 ◽

Cited By ~ 2

Author(s):

Xianchun Zou ◽

Guijun Wang ◽

Guoxian Yu

Keyword(s):

Deep Learning ◽

Language Processing ◽

Protein Function ◽

Homo Sapiens ◽

Protein Function Prediction ◽

Restricted Boltzmann Machines ◽

Boltzmann Machines ◽

Functional Annotations ◽

Learning Techniques ◽

Wide Range

Accurately annotating biological functions of proteins is one of the key tasks in the postgenome era. Many machine learning based methods have been applied to predict functional annotations of proteins, but this task is rarely solved by deep learning techniques. Deep learning techniques recently have been successfully applied to a wide range of problems, such as video, images, and nature language processing. Inspired by these successful applications, we investigate deep restricted Boltzmann machines (DRBM), a representative deep learning technique, to predict the missing functional annotations of partially annotated proteins. Experimental results onHomo sapiens,Saccharomyces cerevisiae,Mus musculus,andDrosophilashow that DRBM achieves better performance than other related methods across different evaluation metrics, and it also runs faster than these comparing methods.

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Ensemble Classification Method for Imbalanced Data Using Deep Learning

The Ecosystem of e-Business: Technologies, Stakeholders, and Connections - Lecture Notes in Business Information Processing ◽

10.1007/978-3-030-22784-5_16 ◽

2019 ◽

pp. 162-170 ◽

Cited By ~ 1

Author(s):

Yoon Sang Lee

Keyword(s):

Deep Learning ◽

Imbalanced Data ◽

Ensemble Classification ◽

Classification Method

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A Beginner's Tutorial of Restricted Boltzmann Machines

10.20944/preprints202003.0337.v1 ◽

2020 ◽

Author(s):

Yiping Cheng

Keyword(s):

Deep Learning ◽

Building Blocks ◽

Learning Networks ◽

Restricted Boltzmann Machines ◽

Boltzmann Machines

Restricted Boltzmann machines (RBMs) are the building blocks of some deep learning networks. However, despite their importance, it is our perception that some very important derivations about the RBM are missing in the literature, and a beginner may feel RBM very hard to understand. We provide here these missing derivations. We cover the classic Bernoulli-Bernoulli RBM and the Gaussian-Bernoulli RBM, but leave out the ``continuous'' RBM as it is believed not as mature as the former two. This tutorial can be used as a companion or complement to the famous RBM paper ``Training restricted Boltzmann machines: An introduction'' by Fisher and Igel.

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