Invention Grant
- Patent Title: Unsupervised learning utilizing sequential output statistics
-
Application No.: US15621753Application Date: 2017-06-13
-
Publication No.: US10776716B2Publication Date: 2020-09-15
- Inventor: Yu Liu , Jianshu Chen , Li Deng
- Applicant: Microsoft Technology Licensing, LLC
- Applicant Address: US WA Redmond
- Assignee: Microsoft Technology Licensing, LLC
- Current Assignee: Microsoft Technology Licensing, LLC
- Current Assignee Address: US WA Redmond
- Agency: Schwegman Lundberg & Woessner, P.A.
- Main IPC: G06N20/00
- IPC: G06N20/00 ; G10L15/06 ; G10L15/197 ; G06N7/00

Abstract:
In classification tasks applicable to data that exhibit sequential output statistics, a classifier may be trained in an unsupervised manner based on a sequence of input samples and an unaligned sequence of output labels, using a cost function that measures the negative cross-entropy of an N-gram joint probability distribution derived from the sequence of output labels with respect to an expected N-gram frequency in a second sequence of output labels predicted by the classifier. In some embodiments, a primal-dual reformulation of the cost function is employed to facilitate optimization.
Public/Granted literature
- US20180357566A1 UNSUPERVISED LEARNING UTILIZING SEQUENTIAL OUTPUT STATISTICS Public/Granted day:2018-12-13
Information query