Kernel methods provide a powerful and unified framework for pattern discovery, motivating algorithms that can act on general types of data (e.g. strings, vectors or text) and look for general types of relations (e.g. rankings, classifications, regressions, clusters). The application areas range from neural networks and pattern recognition to machine learning and data mining. Th...(展开全部)

Kernel methods provide a powerful and unified framework for pattern discovery, motivating algorithms that can act on general types of data (e.g. strings, vectors or text) and look for general types of relations (e.g. rankings, classifications, regressions, clusters). The application areas range from neural networks and pattern recognition to machine learning and data mining. Th...(展开全部)

The analysis of time series data is essential to many areas of science, engineering, finance and economics. This introduction to wavelet analysis "from the ground level and up," and to wavelet-based statistical analysis of time series focuses on practical discrete time techniques, with detailed descriptions of the theory and algorithms needed to understand and implement the dis...(展开全部)

This introduction to wavelet analysis 'from the ground level and up', and to wavelet-based statistical analysis of time series focuses on practical discrete time techniques, with detailed descriptions of the theory and algorithms needed to understand and implement the discrete wavelet transforms. Numerous examples illustrate the techniques on actual time series. The many embedd...(展开全部)

This book provides a comprehensive introduction to methods and models for categorical data analysis and their applications in social science research. An explicit aim of the book is to integrate the transformational and the latent variable approach, two diverse but complementary traditions dealing with the analysis of categorical data. This is the first introductory text to cov...(展开全部)

[美]丹尼尔•A.鲍威斯（Daniel A. Powers 美国得克萨斯大学奥斯汀分校社会学系副教授和人口研究中心研究员。其研究领域包括：应用统计学和研究方法、社会人口学、社会分层、生育和死亡研究，最近的研究主要是婴儿死亡的种族差异和非线性模型的分解技术。主要著作有《分类数据分析的统计方法》。 谢宇（Xie Yu），美国密歇根大学的Otis Dudley Duncan杰出教授，同时担任密歇根大学社会学系、统计系和中国研究中心的教授，社会研究院（ISR）人口研究中心和调查研究中心的研究员，调查研究中心量化方法组主任。2004年当选美国艺术与科学院院士和“台湾中央研究院”院士。其研究领域包括：社会分层、统计方法、人口学、科学社会学和中国研究。主要著作有《分类数据分析的统计方法》、《科学界的女性》、《美国亚裔的人口统计描述》、《社会学方法与定量研究》、《婚...(展开全部)