@article{933026,
      author = {Zhang, Xianda,},
      url = {http://library.usi.edu/record/933026},
      title = {A matrix algebra approach to artificial intelligence /},
      abstract = {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.},
      recid = {933026},
      pages = {1 online resource (xxxiv, 820 pages) :},
}