Programming Collective Intelligence
Programming Collective Intelligence: Building Smart Web 2.0 Applications
- 作者: Toby Segaran
- 出版社/メーカー: O'Reilly Media
- 発売日: 2007/08/26
- メディア: ペーパーバック
- 購入: 3人 クリック: 117回
- この商品を含むブログ (31件) を見る
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
