scholarly journals Relational learning re-examined

1997 ◽  
Vol 20 (1) ◽  
pp. 83-83
Author(s):  
Chris Thornton ◽  
Andy Clark
Keyword(s):  
Incremental Learning ◽  
Relational Learning ◽  
Learning Algorithms ◽  
Trade Off ◽  
Call Type ◽  
Computational Search ◽  
Representational Redescription

We argue that existing learning algorithms are often poorly equipped to solve problems involving a certain type of important and widespread regularity that we call “type-2 regularity.” The solution in these cases is to trade achieved representation against computational search. We investigate several ways in which such a trade-off may be pursued including simple incremental learning, modular connectionism, and the developmental hypothesis of “representational redescription.”

Sliding mode incremental learning algorithm for interval type-2 Takagi–Sugeno–Kang fuzzy neural networks

2012 ◽  
Vol 3 (3) ◽  
pp. 179-188 ◽  
Author(s):  
Sevil Ahmed ◽  
Nikola Shakev ◽  
Andon Topalov ◽  
Kostadin Shiev ◽  
Okyay Kaynak
Keyword(s):  
Neural Networks ◽  
Incremental Learning ◽  
Sliding Mode ◽  
Learning Algorithm ◽  
Fuzzy Neural Networks ◽  
Fuzzy Neural ◽  
Interval Type ◽  
Takagi Sugeno

Mining Live Transliterations Using Incremental Learning Algorithms

2008 ◽  
Vol 21 (02) ◽  
pp. 183-203 ◽  
Author(s):  
HAIZHOU LI ◽  
JIN-SHEA KUO ◽  
JIAN SU ◽  
CHIH-LUNG LIN
Keyword(s):  
Incremental Learning ◽  
Learning Algorithms

scholarly journals Cost-Accuracy Aware Adaptive Labeling for Active Learning

2020 ◽  
Vol 34 (03) ◽  
pp. 2569-2576
Author(s):  
Ruijiang Gao ◽  
Maytal Saar-Tsechansky
Keyword(s):  
Active Learning ◽  
Lower Cost ◽  
State Of The Art ◽  
Learning Algorithms ◽  
Budget Constraint ◽  
Fixed Cost ◽  
Trade Off ◽  
Generalization Performance ◽  
Generalization Bound ◽  
Generalization Accuracy

Conventional active learning algorithms assume a single labeler that produces noiseless label at a given, fixed cost, and aim to achieve the best generalization performance for given classifier under a budget constraint. However, in many real settings, different labelers have different labeling costs and can yield different labeling accuracies. Moreover, a given labeler may exhibit different labeling accuracies for different instances. This setting can be referred to as active learning with diverse labelers with varying costs and accuracies, and it arises in many important real settings. It is therefore beneficial to understand how to effectively trade-off between labeling accuracy for different instances, labeling costs, as well as the informativeness of training instances, so as to achieve the best generalization performance at the lowest labeling cost. In this paper, we propose a new algorithm for selecting instances, labelers (and their corresponding costs and labeling accuracies), that employs generalization bound of learning with label noise to select informative instances and labelers so as to achieve higher generalization accuracy at a lower cost. Our proposed algorithm demonstrates state-of-the-art performance on five UCI and a real crowdsourcing dataset.

Constraining solution space to improve generalization

1997 ◽  
Vol 20 (1) ◽  
pp. 67-68
Author(s):  
John A. Bullinaria
Keyword(s):  
Learning Algorithms ◽  
Solution Space ◽  
Local Information ◽  
Learning Processes ◽  
Point Of View ◽  
Heavy Duty ◽  
Hidden Regularities ◽  
Evolutionary Point

I suggest that the difficulties inherent in discovering the hidden regularities in realistic (type-2) problems can often be resolved by learning algorithms employing simple constraints (such as symmetry and the importance of local information) that are natural from an evolutionary point of view. Neither “heavy-duty nativism” nor “representational recoding” appear to offer totally appropriate descriptions of such natural learning processes.

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