Programming Collective Intelligence - Segaran, Toby

Programming Collective Intelligence

Building Smart Web 2.0 Applications

Toby Segaran 

Herausgeber: O`Brien, Mary Treseler
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Sprache: Englisch
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Programming Collective Intelligence

Want to tap the power behind search rankings, product recommendations, social bookmarking, and online matchmaking? This fascinating book demonstrates how you can build Web 2.0 applications to mine the enormous amount of data created by people on the Internet. With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you've found it.

Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application. This book explains:

- Collaborative filtering techniques that enable online retailers to recommend products or media

- Methods of clustering to detect groups of similar items in a large dataset

- Search engine features crawlers, indexers, query engines, and the PageRank algorithm

- Optimization algorithms that search millions of possible solutions to a problem and choose the best one

- Bayesian filtering, used in spam filters for classifying documents based on word types and other features

- Using decision trees not only to make predictions, but to model the way decisions are made

- Predicting numerical values rather than classifications to build price models

- Support vector machines to match people in online dating sites

- Non-negative matrix factorization to find the independent features in a dataset

- Evolving intelligence for problem solving - how a computer develops its skill by improving its own code the more it plays a game Each chapter includes exercises for extending the algorithms to make them more powerful

Go beyond simple database-backed applications and put the wealth of Internet data to work for you.

"Bravo! I cannot think of a better way for a developer to first learn these algorithms and methods, nor can I think of a better way for me (an old AI dog) to reinvigorate my knowledge of the details." -- Dan Russell, Google

"Toby's book does a great job of breaking down the complex subject matter of machine-learning algorithms into practical, easy-to-understand examples that can be directly applied to analysis of social interaction across the Web today. If I had this book two years ago, it would have saved precious time going down some fruitless paths." -- Tim Wolters, CTO, Collective Intellect


  • Verlag: O'Reilly Media
  • 2007
  • Ausstattung/Bilder: 2007. XXI, 334 p. w. figs. 233 mm
  • Seitenzahl: 360
  • Englisch
  • Abmessung: 233mm x 177mm x 27mm
  • Gewicht: 592g
  • ISBN-13: 9780596529321
  • ISBN-10: 0596529325
  • Best.Nr.: 22773430
"Das Buch ist einfach spannend! Es behandelt klassische KI-Themen im Rahmen von Web 2.0-Anwendungen, also Filtertechniken, Clustering, Mustererkennung, Ranking, Optimierungsprobleme, Entscheidungsbäume bis hin zu genetischer Programmierung, neuronalen Netzen und vieles mehr. Und jedes Kapitel wird mit mindestens einer vollständigen und lauffähigen Anwendung illustriert, die in Python geschrieben ist. [...] Wenn man ein wenig Interesse an Themen der Künstlichen Intelligenz hat und ein paar Grundkenntnisse in Mathematik und Statistik besitzt, ist das Buch ein wirklicher Gewinn." --, September 2007
Toby Segaran is the author of Programming Collective Intelligence, a very popular O'Reilly title. He was the founder of Incellico, a biotech software company later acquired by Genstruct. He currently holds the title of Data Magnate at Metaweb Technologies and is a frequent speaker at technology conferences.

1. Introduction to Collective Intelligence
- What Is Collective Intelligence?
- What Is Machine Learning?
- Limits of Machine Learning
- Real-Life Examples
- Other Uses for Learning Algorithms

2. Making Recommendations
- Collaborative Filtering
- Collecting Preferences
- Finding Similar Users
- Recommending Items
- Matching Products
- Building a Link Recommender
- Item-Based Filtering
- Using the MovieLens Dataset
- User-Based or Item-Based Filtering?
- Exercises

3. Discovering Groups
- Supervised versus Unsupervised Learning
- Word Vectors
- Hierarchical Clustering
- Drawing the Dendrogram
- Column Clustering
- K-Means Clustering
- Clusters of Preferences
- Viewing Data in Two Dimensions
- Other Things to Cluster
- Exercises

4. Searching and Ranking
- What's in a Search Engine?
- A Simple Crawler
- Building the Index
- Querying
- Content-Based Ranking
- Using Inbound Links
- Learning from Clicks
- Exercises

5. Optimization
- Group Travel
- Representing Solutions
- The Cost Function
- Random Searching
- Hill Climbing
- Simulated Annealing
- Genetic Algorithms
- Real Flight Searches
- Optimizing for Preferences
- Network Visualization
- Other Possibilities
- Exercises

6. Document Filtering
- Filtering Spam
- Documents and Words
- Training the Classifier
- Calculating Probabilities
- A Naïve Classifier
- The Fisher Method
- Persisting the Trained Classifiers
- Filtering Blog Feeds
- Improving Feature Detection
- Using Akismet
- Alternative Methods
- Exercises

7. Modeling with Decision Trees
- Predicting Signups
- Introducing Decision Trees
- Training the Tree
- Choosing the Best Split
- Recursive Tree Building
- Displaying the Tree
- Classifying New Observations
- Pruning the Tree
- Dealing with Missing Data
- Dealing with Numerical Outcomes
- Modeling Home Prices
- Modeling "Hotness"
- When to Use Decision Trees
- Exercises

8. Building Price Models
- Building a Sample Dataset
- k-Nearest Neighbors
- Weighted Neighbors
- Cross-Validation
- Heterogeneous Variables
- Optimizing the Scale
- Uneven Distributions
- Using Real Data-the eBay API
- When to Use k-Nearest Neighbors
- Exercises

9. Advanced Classification: Kernel Methods and SVMs
- Matchmaker Dataset
- Difficulties with the Data
- Basic Linear Classification
- Categorical Features
- Scaling the Data
- Understanding Kernel Methods
- Support-Vector Machines
- Using LIBSVM
- Matching on Facebook
- Exercises

10. Finding Independent Features
- A Corpus of News
- Previous Approaches
- Non-Negative Matrix Factorization
- Displaying the Results
- Using Stock Market Data
- Exercises

11. Evolving Intelligence
- What Is Genetic Programming?
- Programs As Trees
- Creating the Initial Population
- Testing a Solution
- Mutating Programs
- Crossover
- Building the Environment
- A Simple Game
- Further Possibilities
- Exercises

12. Algorithm Summary
- Bayesian Classifier
- Decision Tree Classifier
- Neural Networks
- Support-Vector Machines
- k-Nearest Neighbors
- Clustering
- Multidimensional Scaling
- Non-Negative Matrix Factorization
- Optimization
A. Third-Party Libraries
B. Mathematical Formulas

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