Pattern Recognition and Machine Learning for Self-Study I - Supervised Learning

Info autore

Kenichiro Ishii worked at NTT until 2003, after which he served as a professor at Nagoya University until 2012. He is currently a Professor Emeritus at Nagoya University.

Naonori Ueda worked at NTT until 2023. Since then, he has been serving as an NTT Research Professor.  He has also been concurrently working at RIKEN Center for Advanced Intelligence Project (AIP) as Deputy Director since 2016, and in 2023, he transitioned to a full-time position at RIKEN AIP.

Eisaku Maeda was with NTT until 2017 and has since been serving as a professor at Tokyo Denki University.

Hiroshi Murase worked at NTT until 2003, after which he served as a professor at Nagoya University until 2021. He is currently a Professor Emeritus at the same university.

Dettagli sul prodotto

• Autori: Kenichiro Ishii, Eisaku Maeda, Eisaku et al Maeda, Hiroshi Murase, Naonori Ueda
• Editore: Springer EN
• Lingue: Inglese
• Formato: Copertina rigida
• Pubblicazione: 11.01.2026
• EAN: 9789819514779
• ISBN: 978-981-9514-77-9
• Pagine: 457
• Illustrazioni: X, 457 p. 175 illus., 25 illus. in color., farbige Illustrationen, schwarz-weiss Illustrationen
• Serie: Springer Asia Pacific Mathematics Series
• Categorie: Scienze naturali, medicina, informatica, tecnica > Matematica > Altro; machine learning, Maschinelles Lernen, Deep Learning, Margin, Prototype, Mathematical Applications in Computer Science, support vector machine, convolutional neural network, Potential function, kernel function, decision boundary, Phi function, Fisher’s method, feature vector, nearest neighbor rule, minimum squared error learning, primal and dual representations of perceptrons, parametric and nonparametric learning, generalized linear discriminant function, Karhunen-Loeve expansion, Bayes decision rule, subspace method