2018
DOI: 10.1002/cae.21988
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SAIL—Software system for learning AI algorithms

Abstract: Artificial intelligence (AI) comprises a large spectrum of groups of algorithms: heuristic algorithms for search and planning, formal methods for representation of knowledge and reasoning, algorithms for machine learning and many more. Since these algorithms are complex, there is a need for a system which would enable their application both in everyday work and education processes. This paper describes a software system for learning AI algorithms called SAIL (Software System for AI Learning), which can be used… Show more

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Cited by 8 publications
(5 citation statements)
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“…Currently, it appears that most data sets are comparable, allowing migration learning to be broadly applied across a wide range of machine learning domains. Usually, convolutional neural networks are not trained from scratch since, even with strong hardware performance, training the model takes a long time as the dataset size increases [ 5 ]. Thus, the issue of lengthy training is avoided.…”
Section: Introductionmentioning
confidence: 99%
“…Currently, it appears that most data sets are comparable, allowing migration learning to be broadly applied across a wide range of machine learning domains. Usually, convolutional neural networks are not trained from scratch since, even with strong hardware performance, training the model takes a long time as the dataset size increases [ 5 ]. Thus, the issue of lengthy training is avoided.…”
Section: Introductionmentioning
confidence: 99%
“…Other technical approaches exist to support the instruction of optimization algorithms. The most frequent approach is the simulation of a selected set of specific algorithms [5,12,16], but animation systems for user algorithms [4] also are available. However, the educational goal of both classes of tools is different from ours.…”
Section: Discussionmentioning
confidence: 99%
“…When the sample m x is read, the positive and negative ratios in the sample set at the previous m moments will change, which will inevitably cause the final classification model to have a learning bias for some samples. To reduce the impact of this change, the current ratio of positive and negative samples and the ratio of the loss of the current sample to the total loss are multiplied together and denoted as D m , as in equation (5). Where H…”
Section: Online Data Migration Model Constructionmentioning
confidence: 99%
“…At present, it seems that such similarity exists in the vast majority of data, which allows migration learning to be widely applied to many areas of machine learning. Usually, the training of convolutional neural networks does not start from scratch, because as the size of the dataset increases, the time required to train the model is still long even with good hardware performance [5]. This avoids the problem of a long training time.…”
Section: Introductionmentioning
confidence: 99%