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推荐几本书,关于智能、生命、网络以及数据分析

《GEB-一条永恒的金带》 【美】道格拉斯·霍夫是塔特,

英文是 Gödel, Escher, Bach: An Eternal Golden Braid(DouglasR. Hofstadter)

看豆瓣推荐:http://book.douban.com/subject/1782675/

中文网页版: http://homepage.fudan.edu.cn/~Ayukawa/at/20050606.htm

中文版的实体书是商务印书馆出版,北大翻译,质量很好。

副标题: 集异璧之大成
原作名: Gödel, Escher, Bach: An Eternal Golden Braid
作者: [美]侯世达 / Douglas Hofstadter
译者: 郭维德 等
出版社: 商务印书馆
出版年: 1996-08-01
页数: 1053
定价: 50.20
装帧: 精装
ISBN: 9787100013239

看过盗梦空间的同学会有感触的,电影里面的核心可以在此书中找到”自指”现象

推荐之二:《集体智慧编程》

作者: TOBY SEGARAN
译者: 莫映 / 王开福
出版社: 电子工业出版社
出版年: 2009年1月
页数: 364
定价: 59.8
装帧: 平装
丛书: 博文视点O’reilly系列
ISBN: 9787121075391

博文视点出版的计算机书质量是不错的,这本主要是将数据挖掘、计算统计等方法与实际的web 2.0中的用户数据分析联系在一起,并提供了相应的简单实现。是学习人工智能、机器学习的入门书。

本书以机器学习与计算统计为主题背景,专门讲述如何挖掘和分析Web上的数据和资源,如何分析用户体验、市场营销、个人品味等诸多信息,并得出有用的结论,通过复杂的算法来从Web网站获取、收集并分析用户的数据和反馈信息,以便创造新的用户价值和商业价值。全书内容翔实,包括协作过滤技术(实现关联产品推荐功能)、集群数据分析(在大规模数据集中发掘相似的数据子集)、搜索引擎核心技术(爬虫、索引、查询引擎、PageRank算法等)、搜索海量信息并进行分析统计得出结论的优化算法、贝叶斯过滤技术(垃圾邮件过滤、文本过滤)、用决策树技术实现预测和决策建模功能、社交网络的信息匹配技术、机器学习和人工智能应用等。
  本书是Web开发者、架构师、应用工程师等的绝佳选择。

豆瓣广告:http://book.douban.com/subject/3288908/

英文版叫做: Programming Collective Intelligence, Building Smart Web 2.0 Applications

这两本书我都还没有读完,但是觉得很不错,所有推荐一下,顺便勉励自己好好看看。

Bibliography Entry Types for .bib file

Bibliography Entry Types for .bib file

Entry types

When entering a reference in the bibliography database, the first thing to decide is what type of entry it is. No fixed classification scheme can be complete, but BibTeX provides enough entry types to handle almost any reference reasonably well.

References to different types of publications contain different information; a reference to a journal might include the volume and number of the journal, which is usually not meaningful for a book. Therefore, database entries of different types have different fields for each entry type, the fields are divided into three classes:

Required
omitting the field will produce an error message and may result in a badly formatted bibliography entry. If the required information is not meaningful, you are using the wrong entry type.
Optional
the field’s information will be used if present, but can be omitted without causing any formatting problems. A reference should contain any available information that might help the reader, so you should include the optional field if it is applicable.
Ignored
the field is ignored. BibTeX ignores any field that is not required or optional, so you can include any fields you want in a bibliography entry. It’s often a good idea to put all relevant information about a reference in its bibliography entry – even information that may never appear in the bibliography. For example, if you want to keep an abstract of a paper in a computer file, put it in an ‘abstract’ field in the paper’s bibliography entry. The bibliography database file is likely to be as good a place as any for the abstract, and it is possible to design a bibliography style for printing selected abstracts.

BibTeX ignores the case of letters in the entry type.

Subtopics

article entry

An article from a journal or magazine.

Format:

     @ARTICLE{citation_key,
              required_fields [, optional_fields] }

Required fields: author, title, journal, year

Optional fields: volume, number, pages, month, note, key

book entry

A book with an explicit publisher.

Format:

     @BOOK{citation_key,
           required_fields [, optional_fields] }

Required fields: author or editor, title, publisher, year

Optional fields: volume, series, address, edition, month, note, key

booklet entry

A work that is printed and bound, but without a named publisher or sponsoring institution.

Format:

     @BOOKLET{citation_key,
              required_fields [, optional_fields] }

Required fields: title

Optional fields: author, howpublished, address, month, year, note, key

conference entry

An article in the proceedings of a conference. This entry is identical to the ‘inproceedings’ entry and is included for compatibility with another text formatting system.

Format:

     @CONFERENCE{citation_key,
                 required_fields [, optional_fields] }

Required fields: author, title, booktitle, year

Optional fields: editor, pages, organization, publisher, address, month, note, key

inbook entry

A part of a book, which may be a chapter and/or a range of pages.

Format:

     @INBOOK{citation_key,
             required_fields [, optional_fields] }

Required fields: author or editor, title, chapter and/or pages, publisher, year

Optional fields: volume, series, address, edition, month, note, key

incollection entry

A part of a book with its own title.

Format:

     @INCOLLECTION{citation_key,
                   required_fields [, optional_fields] }

Required fields: author, title, booktitle, year

Optional fields: editor, pages, organization, publisher, address, month, note, key

inproceedings entry

An article in the proceedings of a conference.

Format:

     @INPROCEEDINGS{citation_key,
                    required_fields [, optional_fields] }

Required fields: author, title, booktitle, year

Optional fields: editor, pages, organization, publisher, address, month, note, key

manual entry

Technical documentation.

Format:

     @MANUAL{citation_key,
             required_fields [, optional_fields] }

Required fields: title

Optional fields: author, organization, address, edition, month, year, note, key

mastersthesis entry

A Master’s thesis.

Format:

     @MASTERSTHESIS{citation_key,
                    required_fields [, optional_fields] }

Required fields: author, title, school, year

Optional fields: address, month, note, key

misc entry

Use this type when nothing else seems appropriate.

Format:

     @MISC{citation_key,
           required_fields [, optional_fields] }

Required fields: none

Optional fields: author, title, howpublished, month, year, note, key

phdthesis entry

A PhD thesis.

Format:

     @PHDTHESIS{citation_key,
                required_fields [, optional_fields] }

Required fields: author, title, school, year

Optional fields: address, month, note, key

proceedings entry

The proceedings of a conference.

Format:

     @PROCEEDINGS{citation_key,
                  required_fields [, optional_fields] }

Required fields: title, year

Optional fields: editor, publisher, organization, address, month, note, key

techreport entry

A report published by a school or other institution, usually numbered within a series.

Format:

     @TECHREPORT{citation_key,
                 required_fields [, optional_fields] }

Required fields: author, title, institution, year

Optional fields: type, number, address, month, note, key

unpublished entry

A document with an author and title, but not formally published.

Format:

     @UNPUBLISHED{citation_key,
                  required_fields [, optional_fields] }

Required fields: author, title, note

Optional fields: month, year, key

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Report An Overview of Aritficial Immune Systems

From Evernote:

[Report] An Overview of Aritficial Immune Systems

Clipped from: http://www.artificial-immune-systems.org/Courses/York/lecture1.pdf

Short Lecture Course supplied by Jon Timmis of the University of York, UK (if you use any slides here, please provide a suitable acknowledgment). These lectures form part of a longer course on swarm intelligence, and also assume some knowledge of evolutionary computation. Immune networks are not covered in these slides, though they are on the course. The reason why there are no slides on immune networks is the topic is worked through with the students on a white board by adapting a clonal selection algorithm into an immune network algorithm. Negative selection is also covered on the white board, rather than through lecture slides.

  • Lecture 1: An Overview of Artifcial Immune Systems Lecture PDF
  • Lecture 2: An Introduction to Relevant Immunology Lecture PDF
  • Lecture 3: Immune Modelling : A simple case study Lecture PDF
  • Lecture 4: Engineering Artificial Immune Systems Lecture PDF
  • Lecture 5: Using Clonal Selection for Supervised Learning Lecture PDF
  • Lecture 6: On Artificial Immune Systems and Swarm Intelligence Lecture PDF

第一讲:人工免疫系统概述
Slide的题目是
Collaborative Bio-Inspired Algorithms
Lecture 6: Artificial Immune Systems

Prof Jon Timmis
October 25, 2010

collaborative
adj. 合作的, 协力完成的

Outline:

  1. AIS Background
    1. History
    2. Of interest because?
  2. Thinking about AIS
  3. Basic Immunology
    1. The Immune what?
  4. Engineering Artificial Immune Systems
  5. Interdiscipinary?

What are AIS?
一种定义:AIS是一种受理论免疫学(theoretical immunology)及实验观察得到的免疫功能、原理、模型启发的适应性系统,被应用于多种复杂问题领域。(该定义来自于2002发表的书)
De Castro, L. & Timmis, J. Artificial immune systems: a new computational intelligence approach Springer Verlag, 2002
但是上述的定义已经不能“与时俱进”了,当前发生了一些变化。具体的将在本次lecture中提到。

History

  • 开创性论文——1986年,Farmer等人(J. Doyne Farmer and Norman H. Packard and Alan S. Perelson)从理论免疫学中引出免疫网络的动态模型。
    • 文章中对比了免疫网络和神经网络的差异
    • 建议从计算的角度分析免疫系统IS
  • 1988年Varela(Varela, F. and Coutinho, A. and Dupire, B. and Nelson, N.)进一步对比了免疫、神经系统。
  • 1990年,日本人石田(Ishida, Y.)是第一个将免疫算法应用在解决实际问题。
  • 上世纪90年代中期,Forrest等人提出Self-nonself及反向选择算法,应用于计算机安全领域
  • 上世纪90年代中期,Hunt等人将免疫的思想用于机器学习领域
  • started quite immunological grounded(基础性的工作开始)
    • 1991年Bersini, H. & Varela, F.出版的著作《Hints for adaptive problem solving gleaned from immune networks》
    • 1994年Forrest, S.; Perelson, A.; Allen, L. & Cherukuri, R.提出的self-nonself,同前述
  • Kind of moved away from that, and abstracted more. (有点儿脱离原来的定义、轨迹,变得更加的抽象、隐喻?)
  • 当前,有种趋势是回归免疫学的根本以及更多的交叉(主要的表现是,2007年生物免疫学家在Nature Reviews Immunology上发表的)
    • 例如2007年Cohen的"The immune system computes"
    • “what might we gain by thinking about immune computation”, Cohen,2007(Mr. Timmis的slide中应该是笔误成了2008)

提出问题!
But how do we manage this intreaction to make it worth while for all concerned … ?

接着提出了AIS一些有趣的地方,自适应性、无中心、自学习、动态平衡等特征。

AIS的概念框架(Conceptual Frameworks)

该框架图出自Timmis Jonathan与De Castro合著的书《Artificial immune systems: a new computational intelligence approach》。将当前研究的交叉方法简单描述出来。

这时候枪头一转,问个问题:什么是免疫系统(Immune System, IS)?
有三种不同角度的解释:

  1. Classic View
    • a complex system of celluar and molecular components having the primary function of distinguishing self from not self and defense against foreign organisms or substances.
    • 上面的翻译:是一种复杂系统,由细胞、分子等组成,主要的功能是从“非我”中区分出“自我”(而不是去查找“非我”,记忆、查找的空间会更小),并对外来有机物或者物质起到防御作用
  2. Cognitive View(认知角度)
    • The immune system is a cognitive system whose primary role is to provide body maintenance.[2]
    • 免疫系统是一种认知系统,扮演着提供机体维护的角色[2]
  3. Danger View
    • The immune system recognises dangerous agents and not non-self [9]
    • 发现威胁性物体,而不是关注于“非我”的死死寻觅 [9]

Classic view 代表的是clonal slection 和negative selection等方法。

AIS的工程应用

提到的论文,及其相关信息和摘要:

[1]Bersini, H. & Varela, F. Schwefel, H.-P. & Männer, R. (Eds.) Hints for adaptive problem solving gleaned from immune networks Parallel Problem Solving from Nature, Springer Berlin / Heidelberg, 1991, 496, 343-354
[2] Cohen, I. R. On Tending Adam’s Garden Tending Adam’s Garden, Academic Press, 2000, 243 – 255
[3] Cohen, I. R. Real and artificial immune systems: computing the state of the body Nature Reviews Immunology, 2007, 07, 569-574
[4] De Castro, L. & Timmis, J. Artificial immune systems: a new computational intelligence approach Springer Verlag, 2002
[5] Farmer, J. D.; Packard, N. H. & Perelson, A. S. The immune system, adaptation, and machine learning Physica D: Nonlinear Phenomena, 1986, 22, 187 – 204
Abstract: The immune system is capable of learning, memory, and pattern recognition. By employing genetic operators on a time scale fast enough to observe experimentally, the immune system is able to recognize novel shapes without preprogramming. Here we describe a dynamical model for the immune system that is based on the network hypothesis of Jerne, and is simple enough to simulate on a computer. This model has a strong similarity to an approach to learning and artificial intelligence introduced by Holland, called the classifier system. We demonstrate that simple versions of the classifier system can be cast as a nonlinear dynamical system, and explore the analogy between the immune and classifier systems in detail. Through this comparison we hope to gain insight into the way they perform specific tasks, and to suggest new approaches that might be of value in learning systems.
[6] Forrest, S.; Perelson, A.; Allen, L. & Cherukuri, R. Self-nonself discrimination in a computer Research in Security and Privacy, 1994. Proceedings., 1994 IEEE Computer Society Symposium on, 1994, 202 -212
Abstract: The problem of protecting computer systems can be viewed generally as the problem of learning to distinguish self from other. The authors describe a method for change detection which is based on the generation of T cells in the immune system. Mathematical analysis reveals computational costs of the system, and preliminary experiments illustrate how the method might be applied to the problem of computer viruses
[7] Hunt, J. E. & Cooke, D. E. Learning using an artificial immune system Journal of Network and Computer Applications, 1996, 19, 189 – 212
Abstract: In this paper we describe an artificial immune system (AIS) which is based upon models of the natural immune system. This natural system is an example of an evolutionary learning mechanism which possesses a content addressable memory and the ability to forget little-used information. It is also an example of an adaptive non-linear network in which control is decentralized and problem processing is efficient and effective. As such, the immune system has the potential to offer novel problem solving methods. The AIS is an example of a system developed around the current understanding of the immune system. It illustrates how an artificial immune system can capture the basic elements of the immune system and exhibit some of its chief characteristics. We illustrate the potential of the AIS on a simple pattern recognition problem. We then apply the AIS to a real-world problem: the recognition of promoters in DNA sequences. The results obtained are consistent with other appproaches, such as neural networks and Quinlan’s ID3 and are better than the nearest neighbour algorithm. The primary advantages of the AIS are that it only requires positive examples, and the patterns it has learnt can be explicitly examined. In addition, because it is self-organizing, it does not
require effort to optimize any system parameters.
[8] Ishida, Y. Fully distributed diagnosis by PDP learning algorithm: towards immune network PDP model Neural Networks, 1990., 1990 IJCNN International Joint Conference on, 1990, 777 -782 vol.1
Abstract: Based on the strong analogy between neural networks and distributed diagnosis models, diagnostic algorithms are presented which are similar to the learning algorithm used in neural networks. Diagnostic implications of convergence theorems proved by the Lyapunov function are also discussed. Regarding diagnosis process as a recalling process in the associative memory, a diagnostic method of associative diagnosis is also presented. A good guess of diagnosis is given as a key to recalling the correct diagnosis. The authors regard the distributed diagnosis as an immune network model, a novel PDP (parallel distributed processing) model. This models the recognition capability emergent from cooperative recognition of interconnected units.
[9] Matzinger, P. The Danger Model: A Renewed Sense of Self Science, 2002, 296, 301-305
[10] Neal, M.; Stepney, S.; Smith, R. & Timmis, J. Conceptual frameworks for artificial immune systems International Journal on Unconventional Computing, Old City Publishing, 2005, 1, 315-338
[11] Varela, F.; Coutinho, A.; Dupire, B. & Nelson, N. Cognitive networks: immune, neural and otherwise Addison Wesley Publishing Company, 1988

哈佛图书馆的二十条训言

From Evernote:

哈佛图书馆的二十条训言:

Clipped from: http://blog.renren.com/share/341854935/5381280168

哈佛图书馆的二十条训言:

1、此刻打盹,你将做梦;而此刻学习,你将圆梦。(This moment will nap, you will have a dream; But this moment study,you will interpret a dream. )

2、 我荒废的今日,正是昨日殒身之人祈求的明日。(I leave uncultivated today, was precisely yesterday perishes tomorrow which person of the body implored.)

3、觉得为时已晚的时候,恰恰是最早的时候。(Thought is already is late, exactly is the earliest time.)

4、勿将今日之事拖到明日。(Not matter of the today will drag tomorrow. )

5、学习时的苦痛是暂时的,未学到的痛苦是终生的。(Time the study pain is temporary, has not learned the pain islife-long. )

6、学习这件事,不是缺乏时间,而是缺乏努力。(Studies this matter, lacks the time, but is lacks diligently.)

7、幸福或许不排名次,但成功必排名次。(Perhaps happiness does not arrange the position, but succeeds must arrange the position. )

8、 学习并不是人生的全部。但,既然连人生的一部分——学习也无法征服,还能做什么呢?(The study certainly is not the life complete. But, since continually life part of-studies also is unable to conquer, what butalso can make?)

9、请享受无法回避的痛苦。(Please enjoy the pain which is unable to avoid.)

10、只有比别人更早、更勤奋地努力,才能尝到成功的滋味。(only has compared to the others early, diligently diligently, canfeel the successful taste.)

11、谁也不能随随便便成功,它来自彻底的自我管理和毅力。(Nobody can casually succeed, it comes from the thoroughself-control and the will. )

12、时间在流逝。(The time is passing. )

13、现在流的口水,将成为明天的眼泪。(Now drips the saliva, will become tomorrow the tear.)

14、狗一样地学,绅士一样地玩。(The dog equally study, the gentleman equally plays.)

15、今天不走,明天要跑。(Today does not walk, will have to run tomorrow.)

16、投资未来的人是,忠于现实的人。(The investment future person will be, will be loyal to the realityperson. )

17、教育程度代表收入。(The education level represents the income. )

18、一天过完,不会再来。(one day, has not been able again to come. )

19、即使现在,对手也不停地翻动书页。(Even if the present, the match does not stop changes the page.)

20、没有艰辛,便无所得。(Has not been difficult, then does not have attains’.)

蓝精灵游戏界面截图

发自我的 iPod