Project 1: Classification Using Neural Networks 2009. 03. 23 Kim, Kwonill Biointelligence...

Project 1: Project 1: Classification Using Neural Networks Classification Using Neural Networks

2009. 03. 23Kim, Kwonill

[email protected] Biointelligence laboratory

Artificial Intelligence

mailto:[email protected]

ContentsContents

Project outline Description on the data set Description on tools for ANN Guide to Writing Reports

Style Mandatory contents Optional contents

Submission guide / Marking scheme Demo

2(C) 2008, SNU Biointelligence Laboratory


OutlineOutline

Goal Understand MLP & machine learning deeper Practice researching and technical writing

Handwritten digits problem (classification) To predict the class labels (digits) of handwritten digit data set Using Multi Layer Perceptron (MLP) Estimating several statistics on the dataset

Data set Variation of the ‘Handwritten digit data set’

http://archive.ics.uci.edu/ml/datasets/Pen-Based+Recognition+of+Handwritten+Digits



Handwritten Digit Handwritten Digit Data Set (1/2)Data Set (1/2)

Original Data Set Description Digit database of 11,000 samples from every 44 writers http://archive.ics.uci.edu/ml/datasets/Pen-Based+Recognition+o

f+Handwritten+Digits 16 attributes

(xt, yt), t = 1, … , 8 0 ~ 100

Label (Class) 0, 1, 2, … , 9




Handwritten Digit Handwritten Digit Data Set (2/2)Data Set (2/2)

Constitution Preprocessed data (*.arff, *.csv)

Total data (pendigits_total_set, 1099)= training data (pendigits_training, 749)+ test data (pendigits_test, 350)

Data description (pendigits.names) For WEKA (*.arff)



Tools for Experiments with ANN Tools for Experiments with ANN

Source codes - Choose anything!! Free software Weka (recommended) MATLAB tool box (Toolboxes Neural Network) ANN libraries (C, C++, JAVA, …)

Web sites http://www.cs.waikato.ac.nz/~ml/weka/ http://www.faqs.org/faqs/ai-faq/neural-nets/part5/

Reports StyleReports Style

English only!! Scientific journal-style

How to Write A Paper in Scientific Journal Style and Format http://abacus.bates.edu/~ganderso/biology/resources/writing/HTWsections.html


Experimental process Section of PaperWhat did I do in a nutshell? Abstract

What is the problem? Introduction

How did I solve the problem? Materials and Methods

What did I find out? Results

What does it mean? Discussion

Who helped me out? Acknowledgments (optional)

Whose work did I refer to? Literature Cited

Extra Information Appendices (optional)

Report Contents – Mandatory (1/2)Report Contents – Mandatory (1/2)

System description Used software and running environments

Result graphs and tables

Analysis & discussion (Very Important!!)


Report Contents – Mandatory (2/2)Report Contents – Mandatory (2/2)

Basic experiments Changing # of epochs (Draw learning curve)

Various # of Hidden Units


# Hidden Units

Train TestAverage Std. Dev.

Best Worst Average Std. Dev.

Best Worst

Setting 1 accuracySetting 2Setting 3

Report Contents – OptionalReport Contents – Optional

Various experimental settings Normalization Learning rates Structure of MLP Feature selection Activation functions Learning algorithm …

Evaluation techniques ROC curve k-fold Crossvalidation …



Submission GuideSubmission Guide Due date: Apr. 15th (Wed.) 15:00 Submit both ‘hardcopy’ and ‘email’

Hardcopy submission to the office (301-419 ) E-mail submission to [email protected]

Subject : [AI Project1 Report] Student number, Name Length: report should be summarized within 12 pages. If you build a program by yourself, submit the source code with comments

We are NOT interested in the accuracy and your programming skill,

but your creativity and research ability. If your major is not a C.S, team project with a C.S major student is

possible (Use the class board to find your partner and notice the information of your team to the 1st project TA([email protected]) by Mar. 27th)

Marking Scheme Marking Scheme

40 points for experiment & analysis Extra 4 points for additional expriments

20 points for report 6 points for overall organization Late work

- 10% per one day Maximum 7 days

* The Maximum Score is Changed


ReferencesReferences

Materials about Weka Weka GUI guide (PPT) Explorer guide (PDF) Experimenter guide (PDF)


WEKA DemoWEKA Demo


MatlabMatlab


QnAQnA

MLP is the simplest form of contemporary neural networks. (you can see other forms in the ‘ANN’ section of Wikipedia:

http://en.wikipedia.org/wiki/Artificial_neural_network)

Neural network is sometimes called as ANN (artificial neural network) to stress the difference with the original neural network in the brain or central nervous system.

Learning in neural networks consists in the optimization of weights by gradient descent process. To get the global optimum, we need to try not just several configurations of parameters, but also various random starting points. When you use weka, you need to try several ‘randomSeed’ for this reason


Project 1: Classification Using Neural Networks 2009. 03. 23 Kim, Kwonill Biointelligence...

Documents

Transcript of Project 1: Classification Using Neural Networks 2009. 03. 23 Kim, Kwonill Biointelligence...