Statistical Learning Basics: Data Mining, Inference, and Prediction: Data Mining, Inference, and Prediction

Author: Hastie
Publisher:
Publish Date: 2004-01-01
Features: With the advent of the computer and information age, the scale and complexity of statistical problems have increased dramatically. Challenges in data storage, organization, and retrieval have led to the emergence of a new field called "data mining." Data mining is an interdisciplinary field involving database technology, machine learning, statistics, neural networks, pattern recognition, knowledge bases, information extraction, high-performance computing, and many other areas, and has been widely applied in numerous industries such as industry, business, finance, communication, healthcare, biotechnology, and science. This book attempts to gather many important new ideas in the field of learning and explain them within the framework of statistics. Although some mathematical details are necessary, the book emphasizes methods and their conceptual foundations rather than theoretical aspects. The content is extensive, covering both supervised (prediction) and unsupervised learning. It includes topics such as neural networks, support vector machines, classification trees, and boosting, making it the most comprehensive book in its category, suitable for readers engaged in data mining and machine learning research. The rapid development of computing and information technology has brought massive amounts of data to various fields such as medicine, biology, finance, and marketing. Understanding this data is a challenge, which has led to the development of new tools in the field of statistics and the extension into new areas such as data mining, machine learning, and bioinformatics. Many tools share a common foundation but are often expressed in different terminology. This book introduces some important concepts in these fields. Although statistical methods are applied, the emphasis is on concepts rather than mathematics. Many examples are accompanied by color illustrations. The content is extensive, covering both supervised (prediction) and unsupervised learning. It includes topics such as neural networks, support vector machines, classification trees, and boosting, making it the most comprehensive book in its category. This book can serve as a textbook for undergraduate and graduate students in relevant fields at universities. For statisticians, researchers in the scientific community, and industry professionals interested in data mining, this book is highly recommended.