Invention Grant
- Patent Title: Recovering the structure of sparse markov networks from high-dimensional data
- Patent Title (中): 从高维数据恢复稀疏马尔科夫网络的结构
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Application No.: US13617558Application Date: 2012-09-14
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Publication No.: US08775345B2Publication Date: 2014-07-08
- Inventor: Narges Bani Asadi , Guillermo A. Cecchi , Dimitri Kanevsky , Bhuvana Ramabhadran , Irina Rish , Katya Scheinberg
- Applicant: Narges Bani Asadi , Guillermo A. Cecchi , Dimitri Kanevsky , Bhuvana Ramabhadran , Irina Rish , Katya Scheinberg
- Applicant Address: US NY Armonk
- Assignee: International Business Machines Corporation
- Current Assignee: International Business Machines Corporation
- Current Assignee Address: US NY Armonk
- Agency: Fleit Gibbons Gutman Bongini & Bianco PL
- Agent Jose Gutman
- Main IPC: G06F17/00
- IPC: G06F17/00 ; G06F7/60 ; G06F3/00 ; G06N99/00

Abstract:
A method, information processing system, and computer readable article of manufacture model data. A first dataset is received that includes a first set of physical world data. At least one data model associated with the first dataset is generated based on the receiving. A second dataset is received that includes a second set of physical world data. The second dataset is compared to the at least one data model. A probability that the second dataset is modeled by the at least one data model is determined. A determination is made that the probability is above a given threshold. A decision associated with the second dataset based on the at least one data model is generated in response to the probability being above the given threshold. The probability and the decision are stored in memory. The probability and the decision are provided to user via a user interface.
Public/Granted literature
- US20130013538A1 RECOVERING THE STRUCTURE OF SPARSE MARKOV NETWORKS FROM HIGH-DIMENSIONAL DATA Public/Granted day:2013-01-10
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