A New Learning Automata Algorithm for Selection of Optimal Subset

2015 ◽  
pp. 693-702
Author(s):  
Xinyi Guo ◽  
Wen Jiang ◽  
Hao Ge ◽  
Shenghong Li
Keyword(s):  
Learning Automata ◽  
Optimal Subset ◽  
New Learning ◽  
Selection Of

A New Learning Automata-Based Pruning Method to Train Deep Neural Networks

2018 ◽  
Vol 5 (5) ◽  
pp. 3263-3269 ◽  
Author(s):  
Haonan Guo ◽  
Shenghong Li ◽  
Bin Li ◽  
Yinghua Ma ◽  
Xudie Ren
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Learning Automata ◽  
Pruning Method ◽  
New Learning

Advocating for Mobile Music Technology

2021 ◽  
pp. 145-157
Author(s):  
Dominic Pisano
Keyword(s):  
Mobile Technology ◽  
Traditional Music ◽  
Learning Opportunities ◽  
Music Technology ◽  
Media Arts ◽  
Music Classroom ◽  
Mobile Music ◽  
New Learning ◽  
The World ◽  
Selection Of

The chapter reviews the benefits of implementing mobile technology in the music classroom. Matters relating to integrating mobile devices in the traditional music rehearsal setting and addressing new learning opportunities in music and media arts are presented. A detailed schedule of a class day with incorporating music technology is offered. Strategies for financing and maintaining mobile technology, as well as ways to avoid common implementation minefields, are given. A large selection of suggested apps useful in the rehearsal space is given. Further, the chapter includes practical, real-world strategies from the field to help teachers successfully adventure into the world of mobile technology in the music classroom.

Goal-oriented optimal subset selection of correlated multimedia streams

2007 ◽  
Vol 3 (1) ◽  
pp. 2 ◽  
Author(s):  
Pradeep K. Atrey ◽  
Mohan S. Kankanhalli ◽  
John B. Oommen
Keyword(s):  
Subset Selection ◽  
Optimal Subset ◽  
Multimedia Streams ◽  
Selection Of

scholarly journals Elements of Technology Enabled/Enhanced Active Learning (TEAL) to Enhance Quality and Employability of Bachelor’s Students

2018 ◽  
Vol 150 ◽  
pp. 05005
Author(s):  
Nur Farha Bte Hassan ◽  
Saifullizam Bin Puteh ◽  
Amanina Binti Muhamad Sanusi
Keyword(s):  
Active Learning ◽  
Teaching And Learning ◽  
Learning Experience ◽  
Employability Skills ◽  
Educational Institutions ◽  
Technical Knowledge ◽  
Learning Content ◽  
New Learning ◽  
Selection Of

The application of technology innovation is rapidly increasing in industries and educational institutions. This phenomenon has led to the emergence of Technology Enabled/Enhanced Active Learning (TEAL) which emphasizes the use of various techniques and technologies. TEAL is a new learning format that combines educational content from a lecturer, simulation, and student’s experiences using technological tools to provide a rich collaborative learning experience for students. This approach is used to provide academic professional development that brings innovation to the learning content, practically by using pedagogy, technology and classroom design. TEAL ensures the enhanced development of student's knowledge and skills in order to produce quality skilful workers with adequate employability skills. Technology is an effective tool used to facilitate the teaching and learning process, which can, in turn, create an active environment for students to build their knowledge, skill and experience. This paper determines the elements of TEAL based on interview sessions with expert academicians and from a systematic literature review. The selection of TEAL elements for this study was carried out using thematic analysis approach. Findings show that these TEAL elements would help institutions to promote students in involving themselves in active learning in order to enhance the quality of graduates in improving their technical knowledge, thereby enhancing their employability skills.

A new Learning Automata based approach for online tracking of event patterns

2014 ◽  
Vol 137 ◽  
pp. 205-211 ◽  
Author(s):  
Wen Jiang ◽  
Cheng-Lin Zhao ◽  
Sheng-Hong Li ◽  
Lawson Chen
Keyword(s):  
Learning Automata ◽  
New Learning ◽  
Online Tracking

WHICH IS THE MOST APPROPRIATE LEARNING MODEL TO IMPROVE STUDENTS’ ENTREPRENEURIAL INTEREST? A COMPARISON OF TWO KINDS OF ACTION RESEARCH

2020 ◽  
Vol 8 (2) ◽  
pp. 109-117
Author(s):  
Inaya Sari Melati ◽  
Ahmad Jenudin
Keyword(s):  
Quantitative Research ◽  
Problem Based Learning ◽  
Learning Model ◽  
Control Group ◽  
Education Students ◽  
Design Data ◽  
Control Group Design ◽  
New Learning ◽  
Post Test ◽  
Selection Of

Purpose of the study: This study introduced a new learning model called GEPPRAK (Grup: Group, Eksplorasi: Exploration, Pengembangan Ide: Idea Development, Penyusunan dan PresentasiRencana Usaha: Preparation and Presentation of the Business Plan, Aksi dan Kompetisi: Action and Competition)and compared higher education students' interest change in entrepreneurship through the application of the GEPPRAK learning model and the Problem Based Learning (PBL) learning model. Methodology: This research was quantitative research with an experimental research design. The design in this study was the true experimental design using pre-test and post-test control group design. Data collection used documentation, observation, and questionnaire. Data analysis techniques in this study used an independent sample t-test using IBM SPSS 22. Main Findings: The results showed that entrepreneurship learning using the GEPPRAK learning model proved to be able to improve entrepreneurial interest better than the Problem Based Learning (PBL) model in the experimental class in this study. Applications of this study: The results of this study can be used by teachers and lecturers as a reference for the selection of entrepreneurship learning models in schools and universities. Novelty/Originality of this study: The GEPPRAK learning model is a new learning model developed in Indonesia so that it still needs to be tested for its effectiveness in improving students’ entrepreneurial interest.

Adaptive selection of query execution strategies by learning automata

1990 ◽  
Vol 50 (3) ◽  
pp. 219-240
Author(s):  
Frank Wm. Tompa ◽  
JoséI. Icaza
Keyword(s):  
Learning Automata ◽  
Query Execution ◽  
Adaptive Selection ◽  
Selection Of
Sign in / Sign up

Export Citation Format

Share Document