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2011. 8. 25. 11:34 Computer Vision
The Discipline of Machine Learning


Tom M. Mitchell (July 2006), School of Computer Science Carnegie Mellon University Pittsburgh


Tom M. Mitchell http://www.cs.cmu.edu/~tom/



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posted by maetel
2010. 12. 14. 20:02 Computer Vision
Michael I. Jordan & Christopher M. Bishop, "Neural Networks", In Tucker, A. B. (Ed.) CRC Handbook of Computer Science, Boca Raton, FL: CRC Press, 1997.
download: http://www.cs.berkeley.edu/~jordan/papers/crc.ps







1. Introduction


Neural network methods have had their greatest impact in problems where statistical issues dominate and where data are easily obtained.

"conjunction of graphical algorithms and probability theory":
A neural network is first and foremost a graph with patterns represented in terms of numerical values attached to the nodes of the graph and transformations between patterns achieved via simple message-passing algorithms. Many neural network architectures, however, are also statistical processors, characterized by making particular probabilistic assumptions about data.


Based on a source of training data, the aim is to produce a statistical model of the process from which the data are generated so as to allow the best predictions to be made for new data.

statistical modeling - density estimation (unsupervised learning), classification & regression

density estimation ("unsupervised learning")
: to model the unconditional distribution of data described by some vector
- to train samples and a network model to build a representation of the probability density 
- to label regions for a new input vector
 
classification & regression ("supervised learning")
: to distinguish between input variables and target variables
- to assign each input vector to one of classes and target variables to class labels 
-> estimation of conditional densities from the joint input-target space



2. Representation

2.1 Density estimation

To form an explicit model of the input density

Gaussian mixture distribution







2.2 Linear regression and linear discriminants






posted by maetel
2010. 12. 11. 01:58 Computer Vision
Michael I. Jordan, Generic constraints on underspecified target trajectories, Proceedings of international conference on neural networks, (1989), 217-225
http://dx.doi.org/10.1109/IJCNN.1989.118584


informed by 함교수님

cf.

Michael I. Jordan



Introduction

connectionist networks

feedforward controller

forward model


activation patterns (in a network)

motor learning

interpretation


visible units & hidden units

The output units of such a network are hidden with respect to learning and yet are visible given their direct connection to the environment.

task space 
articulatory space



Forward models of the environment


The forward modeling approach assumes that the solution to this minimization problem is based on the computation of a gradient.


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posted by maetel
2010. 11. 11. 01:26 Computer Vision
Richard O. Duda, Peter E. Hart, David G. Stork, Pattern Classification (2nd ed-4th print), Wiley-Interscience

RICHARD O. DUDA
PhD, Professor in the Electrical Engineering Department at San Jose State University, San Jose, California.

PETER E. HART
PhD, Chief Executive Officer and President of Ricoh Innovations, Inc. in Menlo Park, California.

PhD, Chief Scientist, also at Ricoh Innovations, Inc.

lectures: 



cf.
Computer Manual in MATLAB to accompany Pattern Classification, 2nd EditionDavid G. Stork ( Ricoh Silicon Valley ), Elad Yom-Tov, John Wiley & Sons, 2004
loyola: 1관 4층 006.4 S885c 2004

DAVID G. STORK
PhD, Chief Scientist at Ricoh Innovations, Inc., and Consulting Professor of Electrical Engineering at Stanford University. A graduate of MIT and the University of Maryland, he is the founder and leader of the Open Mind Initiative and the coauthor, with Richard Duda and Peter Hart, of Pattern Classification, Second Edition, as well as four other books.

PhD, research scientist at IBM Research Lab in Haifa, working on the applications of machine learning to search technologies, bioinformatics, and hardware verification (among others). He is a graduate of Tel-Aviv University and the Technion.



Preface


"(Our purpose is) to give a systematic account of the major topics  in pattern recognition, based on fundamental principles"

pattern recognition

speech recognition
optical character recognition
signal classification

pattern classification
scene analysis
machine learning

handwriting & gesture recognition
lipreading
geological analysis
document searching
recognition of bubble chamber tracks of subatomic particles
human-machine interface - eg. pen-based computing
human and animal nervous systems

neurobiology
psychology

"We address a specific class of problems - pattern recognition problems - and consider the wealth of different techniques that can be applied to it."

"We discuss the relative strengths and weaknesses of various classification techniques"


statistical methods vs. syntactic methods





posted by maetel
2010. 10. 12. 13:09 Computer Vision

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posted by maetel