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编程集体智能-Programming Collective Intelligence文件编号:832

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标题(title):Programming Collective Intelligence
编程集体智能
作者(author):Segaran, Toby
出版社(publisher):O'Reilly Media, Inc
大小(size):2 MB (2528827 bytes)
格式(extension):epub
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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, and ex.  Read more...
Abstract: 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 ex
Table of contents :
Content: Programming Collective Intelligence
preface
Style of Examples
Why Python?
Significant Whitespace
List comprehensions
Open Apis
Overview of the Chapters
Conventions
Using Code Examples
How to Contact Us
Safari® Books Online
Acknowledgments
1. Introduction to Collective Intelligence
What Is Machine Learning?
Limits of Machine Learning
Real-Life Examples
Other Uses for Learning Algorithms
2. Making Recommendations
Collecting Preferences
Finding Similar Users
Pearson Correlation Score
Which Similarity Metric Should You Use?
Ranking the Critics
Recommending Items. Matching ProductsBuilding a del.icio.us Link Recommender
Building the Dataset
Recommending Neighbors and Links
Item-Based Filtering
Getting Recommendations
Using the MovieLens Dataset
User-Based or Item-Based Filtering?
Exercises
3. Discovering Groups
Word Vectors
Counting the Words in a Feed
Hierarchical Clustering
Drawing the Dendrogram
Column Clustering
K-Means Clustering
Clusters of Preferences
Beautiful Soup
Scraping the Zebo Results
Defining a Distance Metric
Clustering Results
Viewing Data in Two Dimensions
Other Things to Cluster
Exercises. 4. Searching and RankingA Simple Crawler
Crawler Code
Building the Index
Finding the Words on a Page
Adding to the Index
Querying
Content-Based Ranking
Word Frequency
Document Location
Word Distance
Using Inbound Links
The PageRank Algorithm
Using the Link Text
Learning from Clicks
Setting Up the Database
Feeding Forward
Training with Backpropagation
Training Test
Connecting to the Search Engine
Exercises
5. Optimization
Representing Solutions
The Cost Function
Random Searching
Hill Climbing
Simulated Annealing
Genetic Algorithms
Real Flight Searches. The minidom PackageFlight Searches
Optimizing for Preferences
The Cost Function
Running the Optimization
Network Visualization
Counting Crossed Lines
Drawing the Network
Other Possibilities
Exercises
6. Document Filtering
Documents and Words
Training the Classifier
Calculating Probabilities
A Na©ve Classifier
A Quick Introduction to Bayes & Theorem
Choosing a Category
The Fisher Method
Combining the Probabilities
Classifying Items
Persisting the Trained Classifiers
Filtering Blog Feeds
Improving Feature Detection
Using Akismet
Alternative Methods
Exercises. 7. Modeling with Decision TreesIntroducing Decision Trees
Training the Tree
Choosing the Best Split
Entropy
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
k-Nearest Neighbors
Defining Similarity
Code for k-Nearest Neighbors
Weighted Neighbors
Subtraction Function
Gaussian Function
Weighted kNn
Cross-Validation
Heterogeneous Variables
Scaling Dimensions. Optimizing the Scale.

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