Programming Collective Intelligence - Segaran, Toby
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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,
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Produktbeschreibung
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
  • Produktdetails
  • Verlag: O'Reilly Media, Inc, USA / O'Reilly Media, Inc.
  • Seitenzahl: 362
  • 2007
  • Ausstattung/Bilder: 1, black & white illustrations
  • Englisch
  • Abmessung: 233mm x 179mm x 22mm
  • Gewicht: 622g
  • ISBN-13: 9780596529321
  • ISBN-10: 0596529325
  • Best.Nr.: 22773430
Autorenporträt
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.
Inhaltsangabe
Inhaltsverzeichnis Preface 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 del.icio.us 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 Index
Rezensionen
"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." -- Schockwellenreiter.de, September 2007