Invention Application
- Patent Title: REFINED LEARNING DATA REPRESENTATION FOR CLASSIFIERS
-
Application No.: US15143792Application Date: 2016-05-02
-
Publication No.: US20170316342A1Publication Date: 2017-11-02
- Inventor: Vojtech Franc , Karel Bartos , Michal Sofka
- Applicant: Cisco Technology, Inc.
- Main IPC: G06N99/00
- IPC: G06N99/00 ; G06F17/11

Abstract:
In one embodiment, a learning machine device initializes thresholds of a data representation of one or more data features, the thresholds specifying a first number of pre-defined bins (e.g., uniform and equidistant bins). Next, adjacent bins of the pre-defined bins having substantially similar weights may be reciprocally merged, the merging resulting in a second number of refined bins that is less than the first number. Notably, while merging, the device also learns weights of a linear decision rule associated with the one or more data features. Accordingly, a data-driven representation for a data-driven classifier may be established based on the refined bins and learned weights.
Information query