Evaluating Learning Algorithms Composed by a Constructive Meta-Learning Scheme for a Rule Evaluation Support Method

2006 ◽  
Author(s):  
Hidenao Abe ◽  
Shusaku Tsumoto ◽  
Miho Ohsaki ◽  
Takahira Yamaguchi
Keyword(s):  
Learning Algorithms ◽  
Learning Scheme ◽  
Meta Learning ◽  
Rule Evaluation

Theoretical and Empirical Analysis of a Spatial EA Parallel Boosting Algorithm

2018 ◽  
Vol 26 (1) ◽  
pp. 43-66 ◽  
Author(s):  
Uday Kamath ◽  
Carlotta Domeniconi ◽  
Kenneth De Jong
Keyword(s):  
Real World ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Real World Data ◽  
Meta Level ◽  
Meta Learning ◽  
Robustness To Noise ◽  
Boosting Algorithm ◽  
Efficient Learning ◽  
Empirical Analyses

Many real-world problems involve massive amounts of data. Under these circumstances learning algorithms often become prohibitively expensive, making scalability a pressing issue to be addressed. A common approach is to perform sampling to reduce the size of the dataset and enable efficient learning. Alternatively, one customizes learning algorithms to achieve scalability. In either case, the key challenge is to obtain algorithmic efficiency without compromising the quality of the results. In this article we discuss a meta-learning algorithm (PSBML) that combines concepts from spatially structured evolutionary algorithms (SSEAs) with concepts from ensemble and boosting methodologies to achieve the desired scalability property. We present both theoretical and empirical analyses which show that PSBML preserves a critical property of boosting, specifically, convergence to a distribution centered around the margin. We then present additional empirical analyses showing that this meta-level algorithm provides a general and effective framework that can be used in combination with a variety of learning classifiers. We perform extensive experiments to investigate the trade-off achieved between scalability and accuracy, and robustness to noise, on both synthetic and real-world data. These empirical results corroborate our theoretical analysis, and demonstrate the potential of PSBML in achieving scalability without sacrificing accuracy.

scholarly journals Evaluating Learning Algorithms to Support Human Rule Evaluation Based on Objective Rule Evaluation Indices

2007 ◽  
Vol 6 ◽  
pp. S285-S296
Author(s):  
H Abe ◽  
S Tsumoto ◽  
M Ohsaki ◽  
T Yamaguchi
Keyword(s):  
Learning Algorithms ◽  
Rule Evaluation

Improving Multi-Robot Coordination by Game-Theoretic Learning Algorithms

2018 ◽  
Vol 27 (07) ◽  
pp. 1860015 ◽  
Author(s):  
Michalis Smyrnakis ◽  
Hongyang Qu ◽  
Sandor M. Veres
Keyword(s):  
Task Allocation ◽  
Cooperative Games ◽  
Learning Algorithms ◽  
Potential Games ◽  
Learning Scheme ◽  
Game Theoretic ◽  
Robot Cooperation ◽  
Shared Objective ◽  
Theoretical Results ◽  
Multi Robot

Cooperative games-based robot cooperation is analysed for reoccurring scenarios. It is shown that potential games can be used for robot coordination when the robots have a shared objective. By observing each others’ behaviour in similar scenarios, they estimate each other’s expected actions, which they use for their own choice of action. The resulting learning scheme can enable “tuning” of smooth cooperation by task allocation in teams of robots for various goals and in reoccurring scenarios of their environment. The theoretical results and methods are illustrated in simulation.

Sign in / Sign up

Export Citation Format

Share Document