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Pattern recognition and machine learning

Tác giả: Christopher M. Bishop

Chapter 1: Introduction. Chapter 2: Probability distributions. Chapter 3: Linear model for regression. Chapter 4: Linear models for classification. Chapter 5: Neural networks. Chapter 6. Kernel methods. Chapter 7: Sparse Kernel machines. Chapter 8: Graphical models. Chapter 9: Mixture models and ...

The data model resource book : vol 1 : a library of universal data models by for all enterprises

Tác giả: Len Silverston

The development of corporate database systems is complex, time-consuming, and expensive, causing developers to look for ways to cut costs. Len Silverston found a way to do this by identifying core data models that most companies share, standardizing them, and making them available on this CD-ROM.

The data model resource book : vol 2 : a library of universal data models by industry types (with CD)

Tác giả: Len Silverston

Chapter 1. Introduction. Chapter 2. Manufacturing. Chapter 3. Telecommunications. Chapter 4. Health care. Chapter 5. Insurance. Chapter 6.Financial services. Chapter 7. Professional services. Chapter 8. Travel. Chapter 9. E-commerce models. Chapter 10. Using the industry medels in the real world.

The data model resource book : vol 3 : universal patterns for data modeling

Tác giả: Len Silverston, Paul Agnew

Chapter 1: Introduction. Chapter 2. Setting up roles: What parties do. Chapter 3. Using roles: How parties are involved. Hierarchies, aggregations, and peer-to-peer relationship: The organization of similar data. Chapter 5. Types and catergories: The classification of data. Chapter 6. Status: The ...

The elements of statistical learning : data mining, inference, and prediction : 2nd ed.

Tác giả: Trevor Hastie; Robert Tibshirani; J H Friedman

Contents: 1. Introduction; 2. Overview of supervised learning; 3. Linear methods for regression; 4. Linear methods for classification; 5. Basis expansions and regularization; 6. Kernel smoothing methods; 7. Model assessment and selection; 8. Model inference and averaging; 9. Additive models, trees, ...