Apple Tree

Linux & Ubuntu/Linux tips | Posted by 알 수 없는 사용자 2008. 5. 7. 16:24

linux command related with process

$ps
    to see currently running process
$kill 1012    #kill {PID}
    to stop any process by PID i.e. to kill process
$killall http    #killall {Process-name}
    to stop processes by name i.e. to kill process
$ps -ag
    to get information about all running process
$kill 0
    to stop all process except your shell
$ls | wc -l &    #linux-command &
    for background processing
$ps aux
    to disply the owner of the processes along with the processes
$ps ax | grep http   #ps ax | grep process-U-want-to see
    to see if a particular process is runing or not. For this purpose you have to use
    ps command in combination with the grep command
$top
    to see currently running processes and other information like memory and
    CPU usage with real time updates.
$pstree
    to display a tree of processes
   
   
Machine Learning | Posted by 알 수 없는 사용자 2008. 4. 15. 22:37

machine learning video lectures


9th Machine Learning Summer School
Machine Learning | Posted by 알 수 없는 사용자 2008. 4. 15. 09:45

a short course on graphical models

I designed this course while I was an intern at the Intel Berkeley Research Center during the summer of 2003. If you find the slides useful, you are welcome to use them (with proper credit). Please let me know if you find any typos or errors.

A Short Course on Graphical Models

Mark A. Paskin

This course covers the basics of graphical models, which are powerful tools for reasoning under uncertainty in large, complex systems. The course assumes little or no mathematical background beyond set theory, and no background knowledge of Probability Theory. The emphasis is on presenting a set of tools that are useful in a large number of applications, and presenting these tools in a rigorous but intuitive way.

The course has three lectures, each of which can be presented at a high level in 90 minutes or split into two 60 minute sessions for more depth.

Lectures Slides
1. Introduction to Probability Theory Motivation, probability spaces, axioms of probability, conditional probability, product rule, chain rule, Bayes' rule, random variables, densities, table densities, Gaussians, marginalization and conditioning, inference. [1, Ch. 1; 5; 2, Ch. 13; 3, Ch. 13; 9]
2. Structured Representations Independence, conditional independence, Bayesian networks, the Bayes Ball algorithm, Markov Random Fields, the Hammersley-Clifford Theorem, moralization, Variable Elimination, NP and #P hardness of inference. [3, Ch. 2; 5; 6; 2, Ch. 14]
3. The junction tree algorithms Junction trees, the Shafer-Shenoy algorithm, its relation to Variable Elimination, the HUGIN algorithm, its relation to Shafer-Shenoy, the Viterbi algorithm, Generalized Distributive Law, triangulation, elimination. [3, Ch. 17; 4]

References for further study
[1] D. Bertsekas and J. Tsitsiklis (2002). Introduction to Probability. Athena Scientific, Belmont, Mass. (First chapter online.)
[2] S. Russell and P. Norvig (2003). Artificial Intelligence: A Modern Approach. Prentice Hall, Englewood Cliffs, NJ.
[3] M. I. Jordan (2003). Introduction to Graphical Models. (Forthcoming.)
[4] R. Cowell, A. Dawid, S. Lauritzen, D. Spiegelhalter (1999). Probabilistic Networks and Expert Systems. Springer, New York, NY.
[5] J. Pearl (1997). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Francisco, CA.
[6] R. Shachter (1998). Bayes-Ball: The Rational Pasttime (for Determining Irrelevance and Requisite Information in Belief Networks and Influence Diagrams). In Gregory F. Cooper and Serafmn Moral (editors), Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann1 San Francisco, CA.



http://www-anw.cs.umass.edu/~cs691t/
Programming/others | Posted by 알 수 없는 사용자 2008. 3. 4. 16:30

PHP Redirect TutorialThe PHP Redirect command

The PHP Redirect command

<?php
header("location: [some-url]");
?>
Replace [some-url] with the URL where you want the redirection to take place.

For example,

header("location: ./version2/index.html"); =>redirect to "index.html" page in
subfolder called "version2"
header("location: http://www.yahoo.com"); =>redirect to a website called yahoo.com
If PHP is not available, it's also possible to use other redirects:

HTTP Redirects
<meta http-equiv="Refresh" content="[time];
URL=[some-url]">

Replace [time] with seconds. This will pause the browser for the specified number of seconds. Replace [some-url] with the target URL you want to redirect.

For example,

<meta http-equiv="Refresh" content="5;
URL=http://www.yahoo.com">

The above HTTP based redirect needs to be in the region of the HTML code.

JavaScript Redirects

<script language=javascript>
setTimeout("location.href='[some-url]'",[time]);
</script>

Replace [time] with milliseconds. This will pause the browser for the specified number of seconds. Replace [some-url] with the target URL you want to redirect.

For example,
setTimeout("location.href='http://www.yahoo.com'", 5000);
The above JavaScript based redirect can be either in the or region of the HTML code.
Usually a PHP redirect is much more reliable than other form of redirects like HTTP redirect or JavaScript based redirects. For example a JavaScript redirect may not work if a user's browser settings has JavaScript turned off.

The reason why PHP redirects will work no matter what settings users have on their browser is because PHP is server side script. It will not depend on browser settings that may affect JavaScript which is parsed on the client-side/user-side.
Linux & Ubuntu/Linux tips | Posted by 알 수 없는 사용자 2008. 2. 27. 13:53

iconv

iconv -f gb2312 -t utf-8 original_file_name > target_file_name

ex :
iconv -f gb2312 -t utf-8 train.CityU.gb > train.CityU.utf8
scientifical writing | Posted by 알 수 없는 사용자 2008. 2. 18. 22:49

Proposal Writing: from njit ( New Jersey's Science & Technology University )

You must have RealPlayer  installed to be able to view all files.


Videos
LECTURE 1
LECTURE 2
LECTURE 3
LECTURE 4
LECTURE 5
LECTURE 6
Videos
LECTURE 7
LECTURE 8
LECTURE 9
LECTURE 10
LECTURE 11
LECTURE 12
Programming/Python | Posted by 알 수 없는 사용자 2008. 2. 15. 23:16

Dive Into Python -- python from novice to pro

[Dive Into Python]

Dive Into Python is a Python book for experienced programmers. You can buy a printed copy, read it online, or download it in a variety of formats. It is also available in multiple languages.

The complete text is available online. You can read the revision history to see what's new. Updated 20 May 2004

Programming/Python | Posted by 알 수 없는 사용자 2008. 2. 15. 22:40

Python的应用



Zope-应用服务器

Plone-内容管理系统

Django-鼓励快速开发的web framework

Twisted - Python Network Application Framework Python的网络应用程序框架

TurboGears - 另一个Web应用快速开发框架

Bit Torrent - 著名的BT下载工具

2006年的Google编程大赛已经将Python作为参赛语言之一
Machine Learning | Posted by 알 수 없는 사용자 2008. 2. 15. 12:38

Extra Readings

[AFDJ03] An introduction to MCMC for machine learning

by C. Andrieu, N. de Freitas, A. Doucet and M. I. Jordan.

Machine Learning, 2003.
[B98] A Tutorial on Support Vector Machines for Pattern Recognition

by Chris Burges.

 KDDM, 1998.
[BBBCL07] Robust Reductions from Ranking to Classification

by Nina Balcan, Nikhil Bansal, Alina Beygelzimer, Don Coppersmith, John Langford, and Greg Sorkin.

COLT 2007.
[BDHLZ05] Reductions Between Classification Tasks

by Alina Beygelzimer, Varsha Dani, Tom Hayes, John Langford and Bianca Zadronzny.

ICML, 2005.
[BKNS04] Policy search by dynamic programming

by J. Andrew Bagnell, Sham Kakade, Andrew Y. Ng and Jeff Schneider.

NIPS 2004.
[BNJ03] Latent Dirichlet allocation

by Dave Blei, Andrew Ng and Michael Jordan.

JMLR, 2003. (You can ignore Section 5 (Inference and Parameter Estimation)).
[GE03] An Introduction to Variable and Feature Selection

by Isabelle Guyon and Andre Elisseeff.

JMLR 2003.
[GS04] Finding scientific topics

by Tom Griffiths and Mark Steyvers.

PNAS, 2004.
[J04] Graphical models

by Michael I. Jordan.

Statistical Science 2004.
[KKJ03] Exploration in Metric State Spaces

by Sham Kakade, Michael Kearns, and John Langford.

ICML 2003.
[KSD06] Learning Low-Rank Kernel Matrices

by Brian Kulis, Matyas Sustik, Inderjit Dhillon.

ICML 2006.
[L03] Gaussian Process Latent Variable Models for Visualisation of High Dimensional Data

by Neil Lawrence.

NIPS 2003.
[L05] Tutorial on Practical Prediction Theory for Classification

by John Langford.

JMLR 2005.
[M03] Simplified PAC-Bayesian Margin Bounds

by David McAllester.

COLT 2003.
[MPKWJW05] Simple Algorithms for Complex Relation Extraction with Applications to Biomedical IE

by R. McDonald, F. Pereira, S. Kulick, S. Winters, Y. Jin, and P. White.

ACL 2005.
[N06] Linear algebra review and reference

by Andrew Ng.

Draft tutorial, 2006.
[NG00] PEGASUS: A policy search method for large MDPs and POMDPs

by Andrew Y. Ng and Michael I. Jordan.

UAI 2000.
[NMM06] Semi-supervised Text Classification Using EM

by Kamal Nigam, Andrew McCallum and Tom Mitchell.

In Semi-supervised Learning, 2006.
[PS07] Policy Gradient Methods for Robotics

by Jan Peters and Stefan Schaal.

IROS 2006.
[Q86] Induction of Decision Trees

by J.R. Quinlan.

MLJ, 1986.
[S99] Perceptron, Winnow, and PAC Learning

by R. Servedio.

COLT 1999.
[SB98] Reinforcement Learning: An Introduction

by Rich Sutton and Andrew Barto.

MIT Press, 1998.
[SM06] An Introduction to Condition Random Fields for Relational Learning

by Charles Sutton and Andrew McCallum.

Book Chapter in Introduction to Statistical Relational Learning, 2006.
[SWHSL06] Spectral methods for dimensionality reduction

by L. Saul, K. Weinberger, J. Ham, F. Sha and D. Lee.

In "Semisupervised learning" 2006.
[T08] Dirichlet Processes

by Yee Whye Teh.

Draft tutorial, 2008.
[TDR07] Bayesian Agglomerative Clustering with Coalescents

by Yee Whye Teh, Hal Daumé III and Daniel Roy

NIPS 2007.
[WBS06] Distance Metric Learning for Large Margin Nearest Neighbor Classification

by Kilian Weinberger, John Blitzer and Lawrence Saul.

NIPS 2006.
[WSZS07] Graph Laplacian methods for large-scale semidefinite programming, with an application to sensor localization

by Kilian Weinberger, Fei Sha, Qihui Zhu and Lawrence Saul.

NIPS 2007.
[ZGL03] Semi-supervised learning using Gaussian fields and harmonic functions

by Xiaojin Zhu, Zoubin Ghahramani, and John Lafferty.

ICML 2003.
Machine Learning | Posted by 알 수 없는 사용자 2008. 2. 14. 16:53

Generative and Discriminative Approaches for Graphical Models

CRF and Perceptron approaches

[LafMcCPer01]
 Authors: John Lafferty, Andrew McCallum, Fernando Pereira
Title: Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data.
Proceedings: International Conference on Machine Learning (ICML-2001), 2001.
Presenter:Karthik

[LafZhuLiu04]
 Authors: John Lafferty, Xiaojin Zhu, Yan Liu
Title: Kernel Conditional Random Fields: Representation and Clique Selection
Proceedings: International Conference on Machine Learning (ICML-2004), 2004.
Presenter:Karthik

[Collins02]
Author: Michael Collins
Title: Discriminative Training Methods for Hidden Markov Models: Theory and Experiments with Perceptron Algorithms.
Proceedings: EMNLP 2002.
Presenter: Ozgur

SVM approaches

[TsoJoaHofAlt05]
 Authors: I. Tsochantaridis, T. Joachims, T. Hofmann, and Y. Altun
Title: Large Margin Methods for Structured and Interdependent Output Variables,
Journal: Journal of Machine Learning Research (JMLR), 6(Sep):1453-1484, 2005.
Presenter: Vikas

[TasGueKol04]
 Authors: Ben Taskar, Carlos Guestrin and Daphne Koller
Title: Max-Margin Markov Networks.
Proceedings: In Advances in Neural Information Processing Systems 16 (NIPS 2003), 2004.
Presenter: Irina

[McAllester06]
Authors: David McAllester
Generalization Bounds and Consistency for Structured Labeling
to appear in Predicting Structured Data,
edited by G. BakIr, T. Hofmann, B. Scholkopf, A. Smola, B. Taskar, and S. V. N. Vishwanathan. 2006
MIT Press.
Presenter: David


Boosting Approaches

[AltHofJoh03]
Authors: Yasemin Altun, Thomas Hofmann & Mark Johnson
Title: Discriminative Learning for Label Sequences via Boosting
Proceedings: Advances in Neural Information Processing Systems (NIPS*15), 2003.
Presenter: Ozgur

[TorMurFre05]
Authors: Antonio Torralba, Kevin Murphy and William Freeman
Title: Contextual Models for Object Detection using Boosted Random Fields
Proceedings: Advances in Neural Information Processing Systems (NIPS*17), 2005.
Presenter: Allie

[Collins04]
Authors: Michael Collins
Title: Discriminative Reranking for Natural Language Parsing.
Proceedings: International Conference on Machine Learning (ICML-2000), 2000.
Presenter: Irina


Decompositional Approaches

[DauMar05]
 Authors: Hal Daume and Daniel Marcu
Title: Learning as Search Optimization: Approximate Large Margin Methods for Structured Prediction
Proceedings: International Conference on Machine Learning (ICML), 2005.
Presenter: Yasemin

[RotYih05]
Authors: D. Roth and W. Yih
Title: Integer Linear Programming Inference for Conditional Random Fields.
Proceedings: International Conference on Machine Learning (ICML)  (2005) pp. 737--744
Presenter: Karthik

[LeCHua05]
Authors: LeCun and Huang
Title: Loss Functions for Discriminative Training of Energy-Based Models
Proceedings: AI-Stats, 2005
Presenter: Allie

[WesChaEliSchVap02]
Authors: J. Weston, O. Chapelle, A. Elisseeff, B. Schoelkopf and V. Vapnik
Title: Kernel Dependency Estimation
Proceedings: NIPS 2002.
Presenter: Vikas

Semi-Supervised/Unsupervised Learning

[AltMcCBel05]
Authors: Yasemin Altun, David McAllester, Misha Belkin.
Title: Maximum Margin Semi-Supervised Learning for Structured Variables
Proceedings: NIPS 2005.

[BreSch06]
Authors: Ulf Brefeld, Tobias Scheffer.
Title: Semi-Supervised Learning for Structured Output Variables,
Proceedings: ICML 2006.

[XuWilSouSch06]
Authors: Linli Xu, Dana Wilkinson, Finnegan Southey, Dale Schuurmans
Title: Discriminative Unsupervised Learning of Structured Predictors
Proceedings: ICML 2006.


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