Invention Grant
US07870082B2 Method for machine learning using online convex optimization problem solving with minimum regret
失效
使用在线凸优化问题求解的机器学习方法以最小的遗憾
- Patent Title: Method for machine learning using online convex optimization problem solving with minimum regret
- Patent Title (中): 使用在线凸优化问题求解的机器学习方法以最小的遗憾
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Application No.: US11759505Application Date: 2007-06-07
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Publication No.: US07870082B2Publication Date: 2011-01-11
- Inventor: Elad Eliezer Hazan , Nimrod Megiddo
- Applicant: Elad Eliezer Hazan , Nimrod Megiddo
- Applicant Address: US NY Armonk
- Assignee: International Business Machines Corporation
- Current Assignee: International Business Machines Corporation
- Current Assignee Address: US NY Armonk
- Agency: Shimokaji & Assoc.
- Main IPC: G06F15/18
- IPC: G06F15/18

Abstract:
Methods, systems, and computer program products are provided for the online convex optimization problem, in which the decision maker has knowledge of the all past states and resulting cost functions for his previous choices and attempts to make a new choice that results in minimum regret. The method does not rely upon the structure of the cost function or the characterization of the states and takes advantage of the similarity between successive states to enable the method to converge to a reasonably optimal result.
Public/Granted literature
- US20080306887A1 METHOD FOR MACHINE LEARNING WITH STATE INFORMATION Public/Granted day:2008-12-11
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