COLLECTIVE INTELLIGENCE TOBY SEGARAN PDF

Shelves: owned Programming Collective Intelligence is easy to read, small but concise, and its only major flaw is the title; and that is because it is misleading. The book touches quite heavily on using collective information and social site APIs, but what it is really about is data mining. In that Programming Collective Intelligence is easy to read, small but concise, and its only major flaw is the title; and that is because it is misleading. In that sense, this book can be taken as a reference guide on data mining.

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Shelves: owned Programming Collective Intelligence is easy to read, small but concise, and its only major flaw is the title; and that is because it is misleading. The book touches quite heavily on using collective information and social site APIs, but what it is really about is data mining. In that Programming Collective Intelligence is easy to read, small but concise, and its only major flaw is the title; and that is because it is misleading.

In that sense, this book can be taken as a reference guide on data mining. Each algorithm and idea is accompanied by Python sources. I personally dislike Python as a language, but the author affirms he chose it intentionally because the algorithms look clear and the source is small, with its purpose unhindered by many language artifacts. The book was so interesting, though, that I plan if I ever find the time : to take all the examples and do them in C , then place them on Github.

The book covers classification and feature extraction, supervised and unsupervised algorithms, filtering and discovery and it also has exercises at the end of each chapter.

Here is a short list: - Making Recommendations - about the way one can use data from user preferences in order to create recommendations. Distance metrics and finding similar items to the ones we like or people with similar tastes.

Supervised and unsupervised methods are described, hierarchical clustering, dendograms, column clustering, K-Means clustering and different methods of visualization. Word frequency, word distance, location of a document, counting methods, artificial neural networks, the Google PageRank algorithm, extraction of information from link text, and learning from user clicks can be found in this chapter.

The chapter talks about optimizing problems like travel schedules and the example uses data from Kayak. You can find here Bayesian filtering and the Fisher method.

This is a great chapter and it show some pretty cool uses of data mining using the Facebook API - Finding Independent Features - reviews Bayesian classification and clustering, then proposes Non-Negative Matrix Factorization, a method invented circa the late 90s, a powerful algorithm which uses matrix algebra to find features in a data set - Evolving Intelligence - bingo!

Genetic Programming made easy. Really cool. An even more concise summary of the methods explained in the book, listing every math formula and obscure library used in the book Conclusion: I really loved the book and I can hardly wait to take it apart with a computer in hand.

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This study further identifies and develops both conceptual and empirical framework for a viable intelligence capability in multi-agency institutions; exploring and suggesting, for the first time, necessary and acceptable conditions for collective intelligence in an environment characterised by conflicting objectives among international actors. It takes as its main premise the view that the United Nations and the world of intelligence are two separate and contradicting entities that conflict in both principles and operating doctrines. From this point of view, the author explores the many theoretical imperatives which set the two institutions apart and stand in the way of understanding the concepts. He also goes further to examine the contexts in which the contradictions could be harmonised.

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Programming Collective Intelligence

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