文件名称:
collective intelligence
开发工具:
文件大小: 3mb
下载次数: 0
上传时间: 2012-10-24
详细说明： Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii 1. Introduction to Collective Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 What Is Collective Intelligence? What Is Machine Learning? Limits of Machine Learning Real-Life Examples Other Uses for Learning Algorithms 2 3 4 5 5 2. Making Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Collaborative Filter ing 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 7 8 9 15 17 19 22 25 27 28 3. Discovering Groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 Supervised versus Unsupervised Learning Word Vectors Hierarchical Clustering Drawing the Dendrogram Column Clustering 29 30 33 38 40 vii K-Means Clustering Clusters of Preferences Viewing Data in Two Dimensions Other Things to Cluster Exercises 42 44 49 53 53 4. Searching and Ranking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 What’s in a Search Engine? A Simple Crawler Building the Index Querying Content-Based Ranking Using Inbound Links Learning from Clicks Exercises 54 56 58 63 64 69 74 84 5. Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 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 87 88 89 91 92 95 97 101 106 110 115 116 6. Document Filtering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 Filtering Spam Documents and Words Training the Classifier Calculating Probabilities A Naïve Classifier The Fisher Method Persisting the Trained Classifiers Filtering Blog Feeds viii | Table of Contents 117 118 119 121 123 127 132 134 Improving Feature Detection Using Akismet Alternative Methods Exercises 136 138 139 140 7. Modeling with Decision Trees . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142 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 142 144 145 147 149 151 153 154 156 158 158 161 164 165 8. Building Price Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167 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 167 169 172 176 178 181 183 189 195 196 9. Advanced Classification: Kernel Methods and SVMs . . . . . . . . . . . . . . . . . . . 197 Matchmaker Dataset Difficulties with the Data Basic Linear Classification Categorical Features Scaling the Data 197 199 202 205 209 Table of Contents | ix Understanding Kernel Methods Support-Vector Machines Using LIBSVM Matching on Facebook Exercises 211 215 217 219 225 10. Finding Independent Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 226 A Corpus of News Previous Approaches Non-Negative Matrix Factorization Displaying the Results Using Stock Market Data Exercises 227 231 232 240 243 248 11. Evolving Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 250 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 250 253 257 259 260 263 265 268 273 276 12. Algorithm Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 277 Bayesian Classifier Decision Tree Classifier Neural Networks Support-Vector Machines k-Nearest Neighbors Clustering Multidimensional Scaling Non-Negative Matrix Factorization Optimization x | Table of Contents 277 281 285 289 293 296 300 302 304 A. Third-Party Libraries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 309 B. Mathematical Formulas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 316 Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323 ...展开收缩
(系统自动生成,下载前可以参看下载内容)
下载文件列表
相关说明
- 本站资源为会员上传分享交流与学习,如有侵犯您的权益,请联系我们删除.
- 本站是交换下载平台，提供交流渠道，下载内容来自于网络，除下载问题外，其它问题请自行百度。
- 本站已设置防盗链，请勿用迅雷、QQ旋风等多线程下载软件下载资源，下载后用WinRAR最新版进行解压.
- 如果您发现内容无法下载，请稍后再次尝试；或者到消费记录里找到下载记录反馈给我们.
- 下载后发现下载的内容跟说明不相乎，请到消费记录里找到下载记录反馈给我们，经确认后退回积分.
- 如下载前有疑问，可以通过点击"提供者"的名字，查看对方的联系方式，联系对方咨询.
