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Programming Collective Intelligence

Cover of Programming Collective Intelligence by Toby Segaran Published by O'Reilly Media, Inc.
  1. Programming Collective Intelligence
    1. SPECIAL OFFER: Upgrade this ebook with O’Reilly
    2. A Note Regarding Supplemental Files
    3. Praise for Programming Collective Intelligence
    4. Preface
      1. Prerequisites
      2. Style of Examples
      3. Why Python?
      4. Open APIs
      5. Overview of the Chapters
      6. Conventions
      7. Using Code Examples
      8. How to Contact Us
      9. Safari® Books Online
      10. Acknowledgments
    5. 1. Introduction to Collective Intelligence
      1. What Is Collective Intelligence?
      2. What Is Machine Learning?
      3. Limits of Machine Learning
      4. Real-Life Examples
      5. Other Uses for Learning Algorithms
    6. 2. Making Recommendations
      1. Collaborative Filtering
      2. Collecting Preferences
      3. Finding Similar Users
      4. Recommending Items
      5. Matching Products
      6. Building a del.icio.us Link Recommender
      7. Item-Based Filtering
      8. Using the MovieLens Dataset
      9. User-Based or Item-Based Filtering?
      10. Exercises
    7. 3. Discovering Groups
      1. Supervised versus Unsupervised Learning
      2. Word Vectors
      3. Hierarchical Clustering
      4. Drawing the Dendrogram
      5. Column Clustering
      6. K-Means Clustering
      7. Clusters of Preferences
      8. Viewing Data in Two Dimensions
      9. Other Things to Cluster
      10. Exercises
    8. 4. Searching and Ranking
      1. What's in a Search Engine?
      2. A Simple Crawler
      3. Building the Index
      4. Querying
      5. Content-Based Ranking
      6. Using Inbound Links
      7. Learning from Clicks
      8. Exercises
    9. 5. Optimization
      1. Group Travel
      2. Representing Solutions
      3. The Cost Function
      4. Random Searching
      5. Hill Climbing
      6. Simulated Annealing
      7. Genetic Algorithms
      8. Real Flight Searches
      9. Optimizing for Preferences
      10. Network Visualization
      11. Other Possibilities
      12. Exercises
    10. 6. Document Filtering
      1. Filtering Spam
      2. Documents and Words
      3. Training the Classifier
      4. Calculating Probabilities
      5. A Naïve Classifier
      6. The Fisher Method
      7. Persisting the Trained Classifiers
      8. Filtering Blog Feeds
      9. Improving Feature Detection
      10. Using Akismet
      11. Alternative Methods
      12. Exercises
    11. 7. Modeling with Decision Trees
      1. Predicting Signups
      2. Introducing Decision Trees
      3. Training the Tree
      4. Choosing the Best Split
      5. Recursive Tree Building
      6. Displaying the Tree
      7. Classifying New Observations
      8. Pruning the Tree
      9. Dealing with Missing Data
      10. Dealing with Numerical Outcomes
      11. Modeling Home Prices
      12. Modeling "Hotness"
      13. When to Use Decision Trees
      14. Exercises
    12. 8. Building Price Models
      1. Building a Sample Dataset
      2. k-Nearest Neighbors
      3. Weighted Neighbors
      4. Cross-Validation
      5. Heterogeneous Variables
      6. Optimizing the Scale
      7. Uneven Distributions
      8. Using Real Data—the eBay API
      9. When to Use k-Nearest Neighbors
      10. Exercises
    13. 9. Advanced Classification: Kernel Methods and SVMs
      1. Matchmaker Dataset
      2. Difficulties with the Data
      3. Basic Linear Classification
      4. Categorical Features
      5. Scaling the Data
      6. Understanding Kernel Methods
      7. Support-Vector Machines
      8. Using LIBSVM
      9. Matching on Facebook
      10. Exercises
    14. 10. Finding Independent Features
      1. A Corpus of News
      2. Previous Approaches
      3. Non-Negative Matrix Factorization
      4. Displaying the Results
      5. Using Stock Market Data
      6. Exercises
    15. 11. EVOLVING INTELLIGENCE
      1. What Is Genetic Programming?
      2. Programs As Trees
      3. Creating the Initial Population
      4. Testing a Solution
      5. Mutating Programs
      6. Crossover
      7. Building the Environment
      8. A Simple Game
      9. Further Possibilities
      10. Exercises
    16. 12. Algorithm Summary
      1. Bayesian Classifier
      2. Decision Tree Classifier
      3. Neural Networks
      4. Support-Vector Machines
      5. k-Nearest Neighbors
      6. Clustering
      7. Multidimensional Scaling
      8. Non-Negative Matrix Factorization
      9. Optimization
    17. A. Third-Party Libraries
      1. Universal Feed Parser
      2. Python Imaging Library
      3. Beautiful Soup
      4. pysqlite
      5. NumPy
      6. matplotlib
      7. pydelicious
    18. B. Mathematical Formulas
      1. Euclidean Distance
      2. Pearson Correlation Coefficient
      3. Weighted Mean
      4. Tanimoto Coefficient
      5. Conditional Probability
      6. Gini Impurity
      7. Entropy
      8. Variance
      9. Gaussian Function
      10. Dot-Products
    19. Index
    20. About the Author
    21. Colophon
    22. SPECIAL OFFER: Upgrade this ebook with O’Reilly
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Support-Vector Machines

Support-vector machines (SVMs) were introduced in Chapter 9, and are probably the most sophisticated classification method covered by this book. SVMs take datasets with numerical inputs and try to predict which category they fall into. You might, for example, want to decide positions for a basketball team from a list of people's heights and running speeds. To simplify, consider just two possibilities—front-court positions in which tall players are required, and back-court positions where you need the faster movers.

An SVM builds a predictive model by finding the dividing line between the two categories. If you plot a set of values for height versus speed and the best position for each person, you get a graph like the one shown in Figure 12-7. Front-court players are shown as Xs and back-court players are shown as Os. Also shown on the graph are a few lines that separate the data into the two categories.

Plot of basketball players and dividing lines

Figure 12-7. Plot of basketball players and dividing lines

A support-vector machine finds the line that separates the data most cleanly, which means that it is the greatest possible distance from points near the dividing line. In Figure 12-7, although all the different lines separate the data, the one that does this best is the one labeled "Best." The only points necessary to determine where the line should be are the points closest to it, and these are known as ...

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