Programming Collective Intelligence: Building Smart Web 2.0 Applications"O'Reilly Media, Inc.", 16. 8. 2007. - 362 страница 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.
"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 |
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Страница xvii
... input from your own users. The ability to harness data cre- ated by people in a variety of ways on different sites is a principle element of creating collective intelligence. A good starting point for finding more web sites with open ...
... input from your own users. The ability to harness data cre- ated by people in a variety of ways on different sites is a principle element of creating collective intelligence. A good starting point for finding more web sites with open ...
Страница 29
... inputs and outputs to learn how to make predictions are known as supervised learning methods. We'll explore many supervised learning methods in this book, including neural networks, decision trees, support-vector machines, and Bayesian ...
... inputs and outputs to learn how to make predictions are known as supervised learning methods. We'll explore many supervised learning methods in this book, including neural networks, decision trees, support-vector machines, and Bayesian ...
Страница 30
Building Smart Web 2.0 Applications Toby Segaran. examining a set of inputs and expected outputs. When we want to extract informa- tion using one of these methods, we enter a set of inputs and expect the application to produce an output ...
Building Smart Web 2.0 Applications Toby Segaran. examining a set of inputs and expected outputs. When we want to extract informa- tion using one of these methods, we enter a set of inputs and expect the application to produce an output ...
Страница 42
... input as does the hierarchical clustering algorithm , along with the number of clusters ( k ) that the caller would like returned . Add this code to clusters.py : AB BED Α ( B A B & D -. 42 Chapter 3 : Discovering Groups K-Means Clustering.
... input as does the hierarchical clustering algorithm , along with the number of clusters ( k ) that the caller would like returned . Add this code to clusters.py : AB BED Α ( B A B & D -. 42 Chapter 3 : Discovering Groups K-Means Clustering.
Страница 74
Достигли сте ограничење за преглед ове књиге.
Достигли сте ограничење за преглед ове књиге.
Садржај
| 1 | |
| 7 | |
| 29 | |
Searching and Ranking | 54 |
Optimization | 86 |
Document Filtering | 117 |
Modeling with Decision Trees | 142 |
Building Price Models | 167 |
Kernel Methods and SVMs | 197 |
Finding Independent Features | 226 |
Evolving Intelligence | 250 |
Algorithm Summary | 277 |
ThirdParty Libraries | 309 |
Mathematical Formulas | 316 |
Index | 323 |
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Programming Collective Intelligence: Building Smart Web 2.0 Applications Toby Segaran Ограничен приказ - 2007 |
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Add this function Akismet algorithm applications average Bayesian classifier blogs branch build calculate chapter collaborative filtering collective intelligence column cost function counts create database dataset decision tree del.icio.us dendrogram determine dictionary dividing documents dot-product email messages entropy Euclidean distance example Facebook features matrix file called flight genetic programming Google hierarchical clustering import input install layer learning LIBSVM linear classifier look loops machine-learning matplotlib method metrics module movie multiplied neighbors neural network node non-negative matrix factorization numpredict numpredict.py NumPy optimization output PageRank parameters parse possible predictions probability problem Python session query random ratings row in rows score search engine shown in Figure shows similar simple solution SQLite Superman Returns support-vector machines urlid variables vector weights matrix wordids words
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Страница 7 - Dec 18 2003, 20:22:39) [MSC v.1200 32 bit (Intel)] on Win32 Type "help", "copyright", "credits" or "license
Страница 232 - The resultant matrix (C) will have the same number of rows as matrix A and the same number of columns as matrix B. 226 "4 2 f 342 6 3 6 * "l 2 3...
Страница 12 - This function will return a value between -1 and 1. A value of 1 means that the two people have exactly the same ratings for every item.
Страница 119 - A probability is a number between 0 and 1, indicating how likely an event is.
Страница 78 - It is easy to extend the network to have more layers by continually using the output of one layer as the input to the next layer.