Invention Grant
- Patent Title: Binary and multi-class classification systems and methods using connectionist temporal classification
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Application No.: US15894883Application Date: 2018-02-12
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Publication No.: US10762891B2Publication Date: 2020-09-01
- Inventor: Saeed Mosayyebpour Kaskari , Trausti Thormundsson , Francesco Nesta
- Applicant: SYNAPTICS INCORPORATED
- Applicant Address: US CA San Jose
- Assignee: SYNAPTICS INCORPORATED
- Current Assignee: SYNAPTICS INCORPORATED
- Current Assignee Address: US CA San Jose
- Agency: Haynes and Boone, LLP
- Main IPC: G10L15/00
- IPC: G10L15/00 ; G10L15/06 ; G06N3/04 ; G06N3/08 ; G10L15/02 ; G10L15/16 ; G10L15/08

Abstract:
A classification training system for binary and multi-class classification comprises a neural network operable to perform classification of input data, a training dataset including pre-segmented, labeled training samples, and a classification training module operable to train the neural network using the training dataset. The classification training module includes a forward pass processing module, and a backward pass processing module. The backward pass processing module is operable to determine whether a current frame is in a region of target (ROT), determine ROT information such as beginning and length of the ROT and update weights and biases using a cross-entropy cost function and connectionist temporal classification cost function. The backward pass module further computes a soft target value using ROT information and computes a signal output error using the soft target value and network output value.
Public/Granted literature
- US20180233130A1 BINARY AND MULTI-CLASS CLASSIFICATION SYSTEMS AND METHODS USING CONNECTIONIST TEMPORAL CLASSIFICATION Public/Granted day:2018-08-16
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