This paper discusses the motivations and principles of Deep Boltzmann Machines regarding learning algorithms for deep architectures. Policy-based systems management (PBM) and Deep Boltzmann Machines (DBM) are two of the many techniques available for artificial intelligence (AI), each having specific benefits and limitations, and thus different applicability; choosing the most appropriate technique is the first of many challenges faced by the developer. The discussion forms a backdrop for a detailed evaluation of the two techniques, in which the concepts underpinning each of PBM and EBM are reviewed and placed into context with each other as well as with the other popular techniques for AI. After considering the operation and suitability of the techniques in isolation, the focus shifts to look at how PBM and DBM could be combined in complementary ways to achieve more sophisticated and versatile AI systems for E-commerce..
목차
Abstract 1. Introduction 2. BackgrounD: RBMS and their Generalizations 3. Multimodal Deep Boltzmann Machine 3.1. Salient Features 3.2. Modeling Tasks 4. In Search of Synergies for E-Commerce 4.1. Dataset and Feature Extraction 4.2. Model 4.3. Classification 4.4. Quantification 5. Conclusion Acknowledgment References
키워드
Deep learningDeep Boltzmann MachinesPolicy-based systems managementE-commerce
저자
Li Min [ Department of Comupter and Information Enginer, Harbin University of commerce ]
Liu Wei [ Department of Comupter and Information Enginer, Harbin University of commerce ]
Xichun Guo [ Department of logistics management, Harbin Railway Technical College ]
보안공학연구지원센터(IJUNESST) [Science & Engineering Research Support Center, Republic of Korea(IJUNESST)]
설립연도
2006
분야
공학>컴퓨터학
소개
1. 보안공학에 대한 각종 조사 및 연구
2. 보안공학에 대한 응용기술 연구 및 발표
3. 보안공학에 관한 각종 학술 발표회 및 전시회 개최
4. 보안공학 기술의 상호 협조 및 정보교환
5. 보안공학에 관한 표준화 사업 및 규격의 제정
6. 보안공학에 관한 산학연 협동의 증진
7. 국제적 학술 교류 및 기술 협력
8. 보안공학에 관한 논문지 발간
9. 기타 본 회 목적 달성에 필요한 사업
간행물
간행물명
International Journal of u- and e- Service, Science and Technology
간기
격월간
pISSN
2005-4246
수록기간
2008~2016
십진분류
KDC 505DDC 605
이 권호 내 다른 논문 / International Journal of u- and e- Service, Science and Technology Vol.7 No.4