Mohlo by sa vám tiež páčiť

Viac informácií o knihe Kernel Methods and Machine Learning

• Parametre knihy
• Anotácia kniha
• Obľúbené z iného súdka

Nákupom získate 278 bodov

Anotácia knihy

Offering a fundamental basis in kernel-based learning theory, this book covers both statistical and algebraic principles. It provides over 30 major theorems for kernel-based supervised and unsupervised learning models. The first of the theorems establishes a condition, arguably necessary and sufficient, for the kernelization of learning models. In addition, several other theorems are devoted to proving mathematical equivalence between seemingly unrelated models. With over 25 closed-form and iterative algorithms, the book provides a step-by-step guide to algorithmic procedures and analysing which factors to consider in tackling a given problem, enabling readers to improve specifically designed learning algorithms, build models for new applications and develop efficient techniques suitable for green machine learning technologies. Numerous real-world examples and over 200 problems, several of which are Matlab-based simulation exercises, make this an essential resource for graduate students and professionals in computer science, electrical and biomedical engineering. Solutions to problems are provided online for instructors.

Parametre knihy

Zaradenie knihy Knihy po anglicky Computing & information technology Computer science Artificial intelligence

• Celý názov: Kernel Methods and Machine Learning
• Autor: S. Y. Kung
• Jazyk: Angličtina
• Väzba: Pevná
• Počet strán: 572
• EAN: 9781107024960
• ISBN: 110702496X
• ID: 02050759
• Nakladateľ: Cambridge University Press
• Hmotnosť: 1354 g
• Rozmery: 255 × 174 × 32 mm
• Dátum vydania: 17. April 2014

Obľúbené z iného súdka

---

---

---