Inexpensive user tracking using Boltzmann machines

E. Mocanu, D.C. Mocanu, H. Bou Ammar, Z. Zivkovic, A. Liotta, E. Smirnov

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

9 Citations (Scopus)
122 Downloads (Pure)

Abstract

Inexpensive user tracking is an important problem in various application domains such as healthcare, human-computer interaction, energy savings, safety, robotics, security and so on. Yet, it cannot be easily solved due to its probabilistic nature, high level of abstraction and uncertainties, on the one side, and to the limitations of our current technologies and learning algorithms, on the other side. In this paper, we tackle this problem by using the Multi-integrated Sensor Technology, which comes at a low price. At the same time, we are aiming to address the lightweight learning requirements by investigating Factored Conditional Restricted Boltzmann Machines (FCRBMs), a form of Deep Learning, that has proven to be an efficient and effective machine learning framework. However, due to their construction properties, the conventional FCRBMs are only capable of performing predictions but are not capable of making classification. Herein, we are proposing extended FCRBMs (eFCRBMs), which incorporate a novel classification scheme, to solve this problem. Experiments performed on both artificially generated as well as real-world data demonstrate the effectiveness and efficiency of the proposed technique. We show that eFCRBMs outperform popular approaches including Support Vector Machines, Naive Bayes, AdaBoost, and Gaussian Mixture Models.
Original languageEnglish
Title of host publicationProceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC2014), 5-8 October, San Diego, California
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Pages1-6
DOIs
Publication statusPublished - 2014
Event2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2014) - San Diego; California, San Diego, United States
Duration: 5 Oct 20148 Oct 2014
Conference number: 6974061
http://ieeexplore.ieee.org/document/6974061/?arnumber=6974061

Conference

Conference2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2014)
Abbreviated titleSMC 2014
CountryUnited States
CitySan Diego
Period5/10/148/10/14
Internet address

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