A New Incremental Learning Technique

1997 ◽  
pp. 112-118 ◽  
Author(s):  
Nick Dunkin ◽  
John Shawe-Taylor ◽  
Pascal Koiran
Keyword(s):  
Incremental Learning ◽  
Learning Technique

scholarly journals Progressive Convolutional Neural Network for Incremental Learning

Electronics ◽  
2021 ◽  
Vol 10 (16) ◽  
pp. 1879
Author(s):  
Zahid Ali Siddiqui ◽  
Unsang Park
Keyword(s):  
Neural Network ◽  
Incremental Learning ◽  
Network Architecture ◽  
Structural Changes ◽  
Training Time ◽  
Learning Technique ◽  
Incremental Training ◽  
Fully Connected ◽  
Fine Tune ◽  
Entire Network

In this paper, we present a novel incremental learning technique to solve the catastrophic forgetting problem observed in the CNN architectures. We used a progressive deep neural network to incrementally learn new classes while keeping the performance of the network unchanged on old classes. The incremental training requires us to train the network only for new classes and fine-tune the final fully connected layer, without needing to train the entire network again, which significantly reduces the training time. We evaluate the proposed architecture extensively on image classification task using Fashion MNIST, CIFAR-100 and ImageNet-1000 datasets. Experimental results show that the proposed network architecture not only alleviates catastrophic forgetting but can also leverages prior knowledge via lateral connections to previously learned classes and their features. In addition, the proposed scheme is easily scalable and does not require structural changes on the network trained on the old task, which are highly required properties in embedded systems.

Incremental learning of word meanings: ERP evidence for stages of meaning acquisition

2010 ◽  
Author(s):  
Gwen A. Frishkoff ◽  
Kevyn Collins-Thompson ◽  
Charles A. Perfetti
Keyword(s):  
Incremental Learning ◽  
Word Meanings

Incremental learning in word production: Tracing the fate of non-selected alternative picture names.

2018 ◽  
Vol 44 (10) ◽  
pp. 1586-1602 ◽  
Author(s):  
Franziska Kurtz ◽  
Herbert Schriefers ◽  
Andreas Mädebach ◽  
Jörg D. Jescheniak
Keyword(s):  
Incremental Learning ◽  
Word Production

Machine Learning of Motor Vehicle Accident Categories from Narrative Data

1996 ◽  
Vol 35 (04/05) ◽  
pp. 309-316 ◽  
Author(s):  
M. R. Lehto ◽  
G. S. Sorock
Keyword(s):  
Machine Learning ◽  
Bayesian Model ◽  
Keyword Search ◽  
Motor Vehicle ◽  
Motor Vehicle Accident ◽  
Computer Search ◽  
Vehicle Accident ◽  
Learning Technique ◽  
Expert Ratings ◽  
Keyword Searches

Abstract:Bayesian inferencing as a machine learning technique was evaluated for identifying pre-crash activity and crash type from accident narratives describing 3,686 motor vehicle crashes. It was hypothesized that a Bayesian model could learn from a computer search for 63 keywords related to accident categories. Learning was described in terms of the ability to accurately classify previously unclassifiable narratives not containing the original keywords. When narratives contained keywords, the results obtained using both the Bayesian model and keyword search corresponded closely to expert ratings (P(detection)≥0.9, and P(false positive)≤0.05). For narratives not containing keywords, when the threshold used by the Bayesian model was varied between p>0.5 and p>0.9, the overall probability of detecting a category assigned by the expert varied between 67% and 12%. False positives correspondingly varied between 32% and 3%. These latter results demonstrated that the Bayesian system learned from the results of the keyword searches.

Developing Scramble-Based Learning Method to Increase Students’ Reading and Writing Comprehension

2019 ◽  
Vol 2 (1) ◽  
pp. 16
Author(s):  
Dani Gunawan
Keyword(s):  
Pilot Study ◽  
Elementary Students ◽  
First Grade ◽  
Learning Method ◽  
Reading And Writing ◽  
Learning Technique ◽  
Increasing Trend

This study was directed to develop a learning technique, to analyze the obstacles faced by teachers in implementing the lesson, and to overcome the problems faced by teachers in enhancing elementary students’ reading and writing comprehension. In order to fulfill the mentioned goals, this study tried to use scramble-based learning technique. It was cconducted at SDN Gentra Masekdas 1, Kecamatan Tarogong Kaler involving 32 first grade students. A pilot study was conducted on 9 March 2017 for about 35 minutes. The first cycle started on 18 April 2017, while the second one was on 24 April 2017. It was found that there was an increasing trend after the implementation. The analysis proccess generated data as followed: during pilot study, eight students succeeded to reach the standard indicator with percentage of 25%. Cycle I generated 15 students with learning completion percentage of 46.8.%. And, during second cycle, there were 27 students who succeeded in reaching completion standard with completion percentage of 84.3%.

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