Học giao tiếp tăng cường: Thuật toán và ứng dụng trong nhận dạng mẫu

Evolutionary Intelligence - Tập 12 - Trang 97-112 - 2019
Aida Chefrour1,2
1LISCO Laboratory, Computer Science Department, Badji Mokhtar-Annaba University, Annaba, Algeria
2Computer Science Department, Mohamed Cherif Messaadia University, Souk Ahras, Algeria

Tóm tắt

Các phương pháp tĩnh hiệu quả nhất trong học máy không cung cấp sự thay thế nào cho quá trình tiến hóa và thích ứng động để tích hợp dữ liệu mới hoặc tái cấu trúc các vấn đề đã được học một phần. Trong lĩnh vực này, học tăng cường đại diện cho một sự thay thế thú vị và là một lĩnh vực nghiên cứu mở, trở thành một trong những mối quan tâm chính của cộng đồng học máy và phân loại. Bài viết này nghiên cứu các kỹ thuật học tăng cường có giám sát và ứng dụng của chúng, đặc biệt trong lĩnh vực nhận dạng mẫu. Bài viết trình bày tổng quan về các khái niệm chính và thuật toán có giám sát của học tăng cường, bao gồm tổng hợp các nghiên cứu đã được thực hiện trong lĩnh vực này và tập trung vào mạng nơ-ron, cây quyết định và máy vector hỗ trợ.

Từ khóa

#học tăng cường #học máy #nhận dạng mẫu #thuật toán #mạng nơ-ron #cây quyết định #máy vector hỗ trợ

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