Evolving Neural Networks in Classification Sunghwan Sohn.

Slides:



Advertisements
Similar presentations
Smartphone-based Activity Recognition for Pervasive Healthcare - Utilizing Cloud Infrastructure for Data Modeling Bingchuan Yuan, John Herbert University.
Advertisements

Biologically Inspired AI (mostly GAs). Some Examples of Biologically Inspired Computation Neural networks Evolutionary computation (e.g., genetic algorithms)
Data Mining Techniques Cluster Analysis Induction Neural Networks OLAP Data Visualization.
The Decision-Making Process IT Brainpower
Organic Computing CS 597 March 8, 2004 Christoph von der Malsburg Computer Science Department University of Southern California and Institute for Neural.
Genetic Algorithms Learning Machines for knowledge discovery.
Text Classification: An Implementation Project Prerak Sanghvi Computer Science and Engineering Department State University of New York at Buffalo.
Intelligent Systems Group Emmanuel Fernandez Larry Mazlack Ali Minai (coordinator) Carla Purdy William Wee.
Soft Computing 1 Neuro-Fuzzy and Soft Computing chapter 1 J.-S.R. Jang Bill Cheetham Kai Goebel.
Electronic and Computer Engineering D Azzi (PL)R Khusainov (SL) D Robinson (RF-HEIF IV)
Presented To: Madam Nadia Gul Presented By: Bi Bi Mariam.
A REVIEW OF FEATURE SELECTION METHODS WITH APPLICATIONS Alan Jović, Karla Brkić, Nikola Bogunović {alan.jovic, karla.brkic,
Chapter 13 Genetic Algorithms. 2 Data Mining Techniques So Far… Chapter 5 – Statistics Chapter 6 – Decision Trees Chapter 7 – Neural Networks Chapter.
INTELLIGENT SYSTEMS MOTIVATIONS M. Gams. Definition (scientific): Intelligent ststem is a system that learns during its existence. It senses its environment.
Introduction to Genetic Algorithms and Evolutionary Computation
Soft Computing Lecture 18 Foundations of genetic algorithms (GA). Using of GA.
TECHNOLOGY GUIDE FOUR Intelligent Systems.
Eduard Petlenkov, Associate Professor, TUT Department of Computer Control
Opportunity. 40 years of the cell phone.
Machine Learning for an Artificial Intelligence Playing Tic-Tac-Toe Computer Systems Lab 2005 By Rachel Miller.
1 Machine Learning 1.Where does machine learning fit in computer science? 2.What is machine learning? 3.Where can machine learning be applied? 4.Should.
Ensembles. Ensemble Methods l Construct a set of classifiers from training data l Predict class label of previously unseen records by aggregating predictions.
Bi-directional incremental evolution Dr Tatiana Kalganova Electronic and Computer Engineering Dept. Bio-Inspired Intelligent Systems Group Brunel University.
Data Mining In contrast to the traditional (reactive) DSS tools, the data mining premise is proactive. Data mining tools automatically search the data.
AN INTELLIGENT AGENT is a software entity that senses its environment and then carries out some operations on behalf of a user, with a certain degree of.
Intelligent Database Systems Lab 國立雲林科技大學 National Yunlin University of Science and Technology 1 Automatic Recommendations for E-Learning Personalization.
1 STAT 5814 Statistical Data Mining. 2 Use of SAS Data Mining.
School of Engineering. Y.U. School of Engineering Founded in Departments Computer Engineering (2001) Industrial Engineering (2001) Electronics.
Intelligent Database Systems Lab N.Y.U.S.T. I. M. Externally growing self-organizing maps and its application to database visualization and exploration.
Mehdi Ghayoumi MSB rm 132 Ofc hr: Thur, a Machine Learning.
INTELLIGENT SYSTEMS INFORMATION SOCIETY MOTIVATIONS M. Gams.
Real-Time Intelligence That Matters. © 2015, Brighterion Inc. (all rights reserved) Keeping an eye on your business The Last G-20 Country To Embrace The.
Pac-Man AI using GA. Why Machine Learning in Video Games? Better player experience Agents can adapt to player Increased variety of agent behaviors Ever-changing.
INTELLIGENT SYSTEMS AND INFORMATION SOCIETY M. Gams Institut Jožef Stefan Ljubljana University.
Computer Vision UNR George Bebis Computer Vision Laboratory (CVL) Department of Computer Science and Engineering University of Nevada, Reno,
DMA 2 KAN Data-flexible multipurpose automated adaptive (k)omplex ANN (or autonomous ANN: A2N2) Neuronal Diversity Why important and prevalent? E.g. aspects:
S. Goonatilake, P. Treleaven: I. S. for Finance and Business 10 years ago substantial increase in I.s. Killer applications - breakthrough Visa, 6 G trans.
DMA 2 KAN Data-flexible multipurpose automated adaptive (k)omplex ANN (or autonomous ANN: A2N2) 2 Data Flexibility The network can deal with many different.

INTELLIGENT SYSTEMS 3. M. Gams. Intelligent systems ENGINEERING, TECHNOLOGY ARTIFICIAL INTELLIGENCE IN. SOCIETY.
Personality Classification: Computational Intelligence in Psychology and Social Networks A. Kartelj, School of Mathematics, Belgrade V. Filipovic, School.
Intelligent Database Systems Lab Presenter : Chang,Chun-Chih Authors : Emilio Corchado, Bruno Baruque 2012 NeurCom WeVoS-ViSOM: An ensemble summarization.
A New Generation of Artificial Neural Networks.  Support Vector Machines (SVM) appeared in the early nineties in the COLT92 ACM Conference.  SVM have.
Smart Web Search Agents Data Search Engines >> Information Search Agents - Traditional searching on the Web is done using one of the following three: -
TECHNOLOGY GUIDE FOUR Intelligent Systems. TECHNOLOGY GUIDE OUTLINE TG4.1 Introduction to Intelligent Systems TG4.2 Expert Systems TG4.3 Neural Networks.
 Negnevitsky, Pearson Education, Lecture 12 Hybrid intelligent systems: Evolutionary neural networks and fuzzy evolutionary systems n Introduction.
Data Mining is the process of analyzing data and summarizing it into useful information Data Mining is usually used for extremely large sets of data It.
Eduard Petlenkov, Associate Professor, TUT Department of Computer Control
OPTIMIZATION OF MODELS: LOOKING FOR THE BEST STRATEGY
Machine Intelligence & Data Science
CS 1010– Introduction to Computer Science
Speaker: Dr. Sridhar Narayan
TECHNOLOGY GUIDE FOUR Intelligent Systems.
RESEARCH APPROACH.
ISS0023 Intelligent Control Systems Arukad juhtimissüsteemid
Welcome To All 18-Sep-18.
INTELLIGENT SYSTEMS BUSINESS MOTIVATION
klinické neurofyziologie
Topological Ordering Algorithm: Example
CH751 퍼지시스템 특강 Uncertainties in Intelligent Systems
This picture is created by using artificial intelligence based hybrid genetic algorithm. (C) 2014 Alfonsas Misevicius.
Pattern recognition in gait activities using a floor sensor system
Decision Making with Neural Networks
کتابهای تازه خریداری شده دروس عمومی 1397
Topological Ordering Algorithm: Example
Decision Making with Neural Networks
Topological Ordering Algorithm: Example
کتابهای خریداری شده دروس عمومی 1397
Topological Ordering Algorithm: Example
Vaal university of technology
Presentation transcript:

Evolving Neural Networks in Classification Sunghwan Sohn

Education Ph.D. Candidate, Engineering Management M.S., Computer Engineering B.E., Electronics Engineering

Experience Research Assistant in Smart Engineering Systems Lab Developing Evolving Neural Networks in Data Mining Research Assistant in Computational Intelligence Lab Developed Automatic White Blood Cell Classification System

Research Objective: To develop a hybrid intelligent system – Evolving Neural Networks (ENNs) – that can be used in data mining, especially in classification problems.

Research (ENNs) Employs computational intelligence methodologies Neural Networks & Genetic Algorithms Genetic algorithms have been applied to automatic generation of neural networks Feature selection Adaptable topology Customized tasks Ensemble method