Continual Learning for Classification Problems: A Survey

2021 ◽  
pp. 156-166
Author(s):  
Mochitha Vijayan ◽  
S. S. Sridhar
Keyword(s):  
Classification Problems ◽  
Continual Learning

scholarly journals Continual Competitive Memory

2021 ◽  
Author(s):  
Alexander G. Ororbia
Keyword(s):  
Unsupervised Learning ◽  
Neural System ◽  
Adaptive Resonance Theory ◽  
Competitive Learning ◽  
Sparse Representations ◽  
Classification Problems ◽  
Neural Models ◽  
Resonance Theory ◽  
Adaptive Resonance ◽  
Continual Learning

In this article, we propose a novel form of unsupervised learning that we call continual competitive memory (CCM) as well as a simple framework to unify related neural models that operate under the principles of competition. The resulting neural system, which takes inspiration from adaptive resonance theory, is shown to offer a rather simple yet effective approach for combating catastrophic forgetting in continual classification problems. We compare our approach to several other forms of competitive learning and find that: 1) competitive learning, in general, offers a promising pathway towards acquiring sparse representations that reduce neural cross-talk, and, 2) our proposed variant, the CCM, which is designed with task streams in mind, is needed to prevent the overriding of old information. CCM yields promising results on continual learning benchmarks including Split MNIST and Split NotMNIST.

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

Continual Learning in a Multi-Layer Network of an Electric Fish

2019 ◽  
Author(s):  
Salomon Z. Muller ◽  
Abigail Zadina ◽  
L.F. Abbott ◽  
Nate Sawtell
Keyword(s):  
Electric Fish ◽  
Continual Learning

Equalization of the Training Set For Backpropagation Networks Applied to Classification Problems

2016 ◽  
Author(s):  
Frederico dos Santos Liporace ◽  
Ricardo José Machado ◽  
Valmir C. Barbosa
Keyword(s):  
Classification Problems ◽  
Training Set
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