利用编程挖掘集体智慧

Collective

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"《编程集体智能》是一本深入探讨如何利用网络上大量用户生成的数据来构建Web 2.0应用程序的书籍。书中介绍了一系列复杂的算法，帮助开发者掌握搜索引擎排名、产品推荐、社会书签和在线匹配等领域的技术。通过这些算法，你可以从其他网站获取有趣的数据集，收集自己应用的用户数据，并进行分析和理解。这本书详细讲解了以下知识点： 1. **协同过滤技术**：这是在线零售商用于推荐商品或媒体的技术，通过分析用户行为来预测个体可能的兴趣。 2. **聚类方法**：在大数据集中检测相似项的群体，有助于理解数据模式。 3. **搜索引擎构建**：包括爬虫、索引器、查询引擎和PageRank算法，这些都是搜索引擎核心功能的实现。 4. **优化算法**：在大量解决方案中寻找最优解，适用于复杂问题的求解。 5. **贝叶斯过滤**：常用于垃圾邮件分类，根据文档中的词性和其他特征对文档进行分类。 6. **决策树**：不仅用于预测，还能模型化决策过程。 7. **数值预测**：建立价格模型，预测未知数值而非分类。 8. **支持向量机（SVM）**：在在线约会网站中匹配人物，通过算法找到最佳匹配。 9. **非负矩阵分解（NMF）**：找出数据集中的独立特征，适用于数据降维和特征提取。 10. **进化智能**：通过不断改进自身代码，使计算机在解决特定问题时逐渐提高技能，例如在游戏中的自我学习。每个章节都包含练习，旨在帮助读者扩展算法，提升其应用能力。本书提供了一种将互联网数据转化为实用工具的方法，对于想要深入理解和应用机器学习算法的开发者来说，是一本宝贵的资源。Google的Dan Russell和Collective Intellect的CTO Tim Wolters都给予了高度评价，认为它能有效帮助开发者快速理解和应用机器学习算法，节省时间和精力。《编程集体智能》是一本涵盖了机器学习、统计学和互联网数据分析的综合指南，适合开发人员、数据分析师和对网络数据挖掘感兴趣的读者。"

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xiv

Preface

Style of Examples

The code examples in each section are written in a tutorial style, which encourages

you to build the applications in stages and get a good appreciation for how the algo-

rithms work. In most cases, after creating a new function or method, you’ll use it in

an interactive session to understand how it works. The algorithms are mostly simple

variants that can be extended in many ways. By working through the examples and

testing them interactively, you’ll get insights into ways that you might improve them

for your own applications.

Why Python?

Although the algorithms are described in words with explanations of the formulae

involved, it’s much more useful (and probably easier to follow) to have actual code

for the algorithms and example problems. All the example code in this book is

written in Python, an excellent, high-level language. I chose Python because it is:

Concise

Code written in dynamically typed languages such as Python tends to be shorter

than code written in other mainstream languages. This means there’s less typing

for you when working through the examples, but it also means that it’s easier to

fit the algorithm in your head and really understand what it’s doing.

Easy to read

Python has at times been referred to as “executable pseudocode.” While this is

clearly an exaggeration, it makes the point that most experienced programmers

can read Python code and understand what it is supposed to do. Some of the less

obvious constructs in Python are explained in the “Python Tips” section below.

Easily extensible

Python comes standard with many libraries, including those for mathematical

functions, XML (Extensible Markup Language) parsing, and downloading web

pages. The nonstandard libraries used in the book, such as the RSS (Really

Simple Syndication) parser and the SQLite interface, are free and easy to down-

load, install, and use.

Interactive

When working through an example, it’s useful to try out the functions as you

write them without writing another program just for testing. Python can run

programs directly from the command line, and it also has an interactive prompt

that lets you type in function calls, create objects, and test packages interactively.

Multiparadigm

Python supports object-oriented, procedural, and functional styles of program-

ming. Machine-learning algorithms vary greatly, and the clearest way to

Preface

xvii

del.icio.us

A social bookmarking application whose open API lets you download links by

tag or from a specific user.

Kayak

A travel site with an API for conducting searches for flights and hotels from

within your own programs.

eBay

An online auction site with an API that allows you to query items that are cur-

rently for sale.

Hot or Not

A rating and dating site with an API to search for people and get their ratings

and demographic information.

Akismet

An API for collaborative spam filtering.

A huge number of potential applications can be built by processing data from a

single source, by combining data from multiple sources, and even by combining

external information with 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

APIs is ProgrammableWeb (http://www.programmableweb.com).

Overview of the Chapters

Every algorithm in the book is motivated by a realistic problem that can, I hope, be

easily understood by all readers. I have tried to avoid problems that require a great

deal of domain knowledge, and I have focused on problems that, while complex, are

easy for most people to relate to.

Chapter 1, Introduction to Collective Intelligence

Explains the concepts behind machine learning, how it is applied in many differ-

ent fields, and how it can be used to draw new conclusions from data gathered

from many different people.

Chapter 2, Making Recommendations

Introduces the collaborative filtering techniques used by many online retailers to

recommend products or media. The chapter includes a section on recommend-

ing links to people from a social bookmarking site, and building a move

recommendation system from the MovieLens dataset.

Chapter 3, Discovering Groups

Builds on some of the ideas in Chapter 2 and introduces two different methods

of clustering, which automatically detect groups of similar items in a large

dataset. This chapter demonstrates the use of clustering to find groups on a set

of popular weblogs and on people’s desires from a social networking web site.

xviii

Preface

Chapter 4, Searching and Ranking

Describes the various parts of a search engine including the crawler, indexer, and

query engine. It covers the PageRank algorithm for scoring pages based on

inbound links and shows you how to create a neural network that learns which

keywords are associated with different results.

Chapter 5, Optimization

Introduces algorithms for optimization, which are designed to search millions of

possible solutions to a problem and choose the best one. The wide variety of

uses for these algorithms is demonstrated with examples that find the best flights

for a group of people traveling to the same location, find the best way of match-

ing students to dorms, and lay out a network with the minimum number of

crossed lines.

Chapter 6, Document Filtering

Demonstrates Bayesian filtering, which is used in many free and commercial

spam filters for automatically classifying documents based on the type of words

and other features that appear in the document. This is applied to a set of RSS

search results to demonstrate automatic classification of the entries.

Chapter 7, Modeling with Decision Trees

Introduces decision trees as a method not only of making predictions, but also of

modeling the way the decisions are made. The first decision tree is built with

hypothetical data from server logs and is used to predict whether or not a user is

likely to become a premium subscriber. The other examples use data from real

web sites to model real estate prices and “hotness.”

Chapter 8, Building Price Models

Approaches the problem of predicting numerical values rather than classifica-

tions using k-nearest neighbors techniques, and applies the optimization

algorithms from Chapter 5. These methods are used in conjunction with the

eBay API to build a system for predicting eventual auction prices for items based

on a set of properties.

Chapter 9, Advanced Classification: Kernel Methods and SVMs

Shows how support-vector machines can be used to match people in online dat-

ing sites or when searching for professional contacts. Support-vector machines

are a fairly advanced technique and this chapter compares them to other methods.

Chapter 10, Finding Independent Features

Introduces a relatively new technique called non-negative matrix factorization,

which is used to find the independent features in a dataset. In many datasets the

items are constructed as a composite of different features that we don’t know in

advance; the idea here is to detect these features. This technique is demon-

strated on a set of news articles, where the stories themselves are used to detect

themes, one or more of which may apply to a given story.

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利用编程挖掘集体智慧

Programming Collective Intelligence（集体智慧编程英文版）

《集体智慧编程》(Programming Collective Intelligence)官方源代码下载

programming collective intelligence

计算机毕设英文参考文献

用学术性的语句改写这句话：近年来，群体智能算法受到了广泛的关注和研究，并 在诸如机器学习、过程控制、工程预测等领域取得了进展。

opencollective-postinstall' 不是内部或外部命令，也不是可运行的程序

integrative collective music

详细介绍一下协同矩阵分解(Collective matrix factorization,CMF)模型

Class "Form" not found

116 packages are looking for funding run `npm fund` for details

ppu 安装 pytorch

what structrue do mpi have

nvidia nccl介绍

php有那些比较好玩的插件

Multi-Agent

旋转翼飞机飞行算法 公式

100 packages are looking for funding run `npm fund` for details

nccl anaconda

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用学术性的语句改写这句话：近年来，群体智能算法受到了广泛的关注和研究，并在诸如机器学习、过程控制、工程预测等领域取得了进展。

旋转翼飞机飞行算法公式