2008 Final Year Project – 1st Term Report - The Chinese University ...

More documents

Recommendations

Info

3.5 RESEARCHED CLASSIFICATION METHODS Department of Computer Science and Engineering, CUHK 2007 – 2008 Final Year Project – 1st Term Report Before we decided to use a particular algorithm for motion classification and recognition, we have studied a number of methods before our final decision. In the 3 sections below we will introduce each of them, the principles behind, and the strength and weaknesses of each of them. 3.5.1 WEKA – A DATA MINING SOFTWARE 3.5.1.1 INTRODUCTION Weka [7] is an open source software implemented by a research group in The University of Waikato. It is a collection of machine learning algorithms for data mining classes. The software contains different tools for data-preprocessing, classification, regression, clustering, association rules and visualization. In the book “Data Mining: Practical machine learning tools and techniques” [7], it is said there are 3 different ways of using Weka – “Apply a learning method to dataset and analyze the output”, “use learned models to generate predictions on new instances”, and “apply several different learners and compare their performance in order to choose one for prediction”. In our project, we used Weka mainly in the 2 nd and 3 rd way – we tried several learners to try on a given data set, and we would like to see which learner performs better based on the predictions on new instances, and eventually we can implement classifiers based on the classification results. LYU0702 Legendary of 18 Weapons - Motion Capture Data Analysis for Wii Remote Page 34 / 77
Department of Computer Science and Engineering, CUHK 2007 – 2008 Final Year Project – 1st Term Report Fig. 3.7 A screenshot of Weka data-preprocessing section 3.5.1.2 TESTED CLASSIFIERS Weka provides different types of classifier algorithms, which includes – Bayes, Trees, Rules, Functions and Lazy. We have selected 5 classifiers, 3 from Trees and 2 from Rules, to investigate and compare their performances. Name Function [7] Trees J48 C4.5 decision tree learner (implements C4.5 revision 8) RandomTree Construct a tree that considers a given number of random features at each node REPTree Fast tree learner that uses reduced-error pruning Rules PART Obtain rules from partial decision trees built using J4.8 Functions Multilayer JRip RIPPER algorithm for fast, effective rule induction Perception A Classifier that uses back propagation to classify instances. Tabel 3.1 Lists of classifiers selected for investigation and their function LYU0702 Legendary of 18 Weapons - Motion Capture Data Analysis for Wii Remote Page 35 / 77
Page 1 and 2: Department of Computer Science and
Page 3 and 4: TABLE OF CONTENTS Department of Com
Page 7 and 8: 1.1 INTRODUCTION TO THE GAME INDUST
Page 9 and 10: 1.3 THE REVOLUTION OF WII Departmen
Page 15 and 16: CHAPTER 2 - WII REMOTE OVERVIEW Dep
Page 17 and 18: 2.2 FUNCTIONALITY Department of Com
Page 19 and 20: 2.2.3 EXPANSION PORT - THE NUNCHUK
Page 23 and 24: CHAPTER 3 - MOTION CLASSIFICATION D
Page 25 and 26: There are several features in LiveM
Page 38 and 39: Name of Attribute Department of Com
Page 41: 3.5.2.3 STRENGTHS AND WEAKNESS Depa
Page 48 and 49: 4.1 TERMINOLOGY Department of Compu
Page 50 and 51: 4.3 CLASSIFICATION WORKING FLOW Dep
Page 54 and 55: 4.7.4 TUNING Department of Computer
Page 56 and 57: � Cursive Writing Letters � A,
Page 60 and 61: - Motion 4 - Forward Department of
Page 62 and 63: 4.7.5.3 RESULT Department of Comput
Page 64 and 65: 4.8 OUR DEVELOPED CONSOLE Departmen
Page 66 and 67: CHAPTER 6 - Department of Computer
Page 72 and 73: CHAPTER 7 - CURRENT LIMITATIONS Dep
Page 74 and 75: CHAPTER 8 - FUTURE WORK semester. D
Page 76 and 77: 8.4 EARLY RECOGNITION MATCHING Depa

2008 Final Year Project – 1st Term Report - The Chinese University ...

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?