PPT-Learning under concept drift: an overview

Zhimin He iTechs ISCAS 20130321 Agenda Whats Concept Drift Causes of a Concept Drift Types of Concept Drift Detecting and Handling Concept Drift Implications for Software Engineering Research. . Chapter. 12-1. Pan-. gaea. . “. all. - earth. ” . Note: Our world today is actually a 2’nd or 3’rd generation “super continent” or “. pangea. ” !!!. . Alfred Wegener . :. R. O. bust. . C. oncept. and . K. nowledge . E. xtraction. from . T. ext. Shi Yu, Glenn Fung . August 2016. SDA. Outlook. Brief Intro to . AmFam. and SDA. Background / Motivation. Potential B. usiness Value. Janice McDonnell, Kristin Hunter-Thomson & Sage . Lichtenwalner. , Rutgers University. Annette . deCharon. , University of Maine. Al Trujillo, Palomar Community College. Catherine . Halversen. , Lawrence Hall of Science. University of Nebraska Medical Center. College of Nursing. What a Concept! . Considering . a Move to . Concept Based Teaching. Definition of terms. Present . current research on the benefits of concept-based . Part 1. Learning Objectives. 1. Describe how variation within groups is maintained and how variation among groups is maintained.. 2. . Describe modern human biological diversity and articulate an informed position on the question of biological races of humans. . ATLANTA WIHS. CHRISTINA MEHTA, PhD. ANNA RUBTSOVA, PhD. Concept Process Overview. “I’ve got a research idea for WIHS. What do I do next?”. Start with Atlanta WIHS website: . http://www.wihs.emory.edu/science/new_concept.html. PhD Student Antigoni P. Anninou. Professor Peter P. Groumpos. Laboratory for Automation and Robotics . Department of Electrical and Computer Engineering. 21st Mediterranean Conference on Control and Automation. ACM Transactions on Computing Education, Vol. 14, No. 3, Article 20. Presented by: Sterling McLeod. Overview. Topics:. Identifying learning problems . Achievement Degree Analysis. Evaluate . how well students comprehend individual concepts in a class to assess a student’s progress in a curriculum. M., Fiore . G., . Gorini. E., . Mazzotta. P., . Miccoli. A., Innocente A., Rocco R., . S. ekhniaidze. G.. 24/06/2013. 1. SUMMARY. TARGET. CONCEPT DESIGN. COMPONENTS CONSTRUCTION. NEXT STEP. 2. TARGET. machine learning implies that a machine will learn how to do something new . but this is . not quite accurate – what is it that the machine is to learn?. is there a process in place and the machine needs to learn domain knowledge? . these tools may offer educators as yet unrealised and potentially complementary functions to aid The over-riding aim of all mapping tools is similar. If students can represent or manipulate a complex Eva García-. Martín. . eva.garcia.martin@bth.se. Thesis. Make Machine Learning algorithms more energy efficient. Machine. . learning. Algorithms that automatically learn with experience. How can one learn from data and process evolving data in only one pass?. J. Carmona. R. Gavaldà. UPC (Barcelona, Spain). 1. Outline. The Advent of Process Mining (PM). T. he challenge of Concept Drift (CD). Key ingredients. Online strategy for CD in PM. Experiments. Work in progress. (Irina Shreyber). 03/06/15. 1. MAD-X Meeting. General Hamiltonian. 03/06/15. 2. MAD-X Meeting. Hamiltonian’s equations of motion. For . a particle moving along an accelerator beam line with coordinates .