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A Matrix Algebra Approach to Artificial Intelligence

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Matrix algebra plays an important role in many core artificial intelligence (AI) areas, including machine learning, neural networks, support vector machines (SVMs) and evolutionary computation. This book offers a comprehensive and in-depth discussion of matrix algebra theory and methods for these four core areas of AI, while also approaching AI from a theoretical matrix algebra perspective.

The book consists of two parts: the first discusses the fundamentals of matrix algebra in detail, while the second focuses on the applications of matrix algebra approaches in AI. Highlighting matrix algebra in graph-based learning and embedding, network embedding, convolutional neural networks and Pareto optimization theory, and discussing recent topics and advances, the book offers a valuable resource for scientists, engineers, and graduate students in various disciplines, including, but not limited to, computer science, mathematics and engineering.

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Informasi Detail

Judul Seri

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No. Panggil

006.3 ZHA a

Penerbit

Singapore : Springer Nature Singapore., 2020

Deskripsi Fisik

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Bahasa

English

ISBN/ISSN

978-981-15-2770-8

Klasifikasi

006.3

Tipe Isi

text

Tipe Media

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Tipe Pembawa

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Edisi

Ed. 1

Subjek

Artifical Intelligence
Special Computer Methods

Info Detail Spesifik

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Pernyataan Tanggungjawab

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Versi lain/terkait

Lampiran Berkas

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