272,966 research outputs found
An investigation of machine learning based prediction systems
Traditionally, researchers have used either o�f-the-shelf models such as COCOMO, or developed local models using statistical techniques such as stepwise regression, to obtain software eff�ort estimates. More recently, attention has turned to a variety of machine learning methods such as artifcial neural networks (ANNs), case-based reasoning (CBR) and rule induction (RI). This paper outlines some comparative research into the use of these three machine learning methods to build software e�ort prediction
systems. We briefly describe each method and then apply the techniques to a dataset of 81 software projects derived from a Canadian software house in the late 1980s. We compare the prediction systems in terms of three factors: accuracy, explanatory value and configurability. We show that ANN methods have superior accuracy and that RI methods are least accurate. However, this view is somewhat counteracted by problems with explanatory value and configurability. For example, we found that considerable
eff�ort was required to configure the ANN and that this compared very unfavourably with the other techniques, particularly CBR and least squares regression (LSR). We suggest that further work be carried out, both to further explore interaction between the enduser and the prediction system, and also to facilitate configuration, particularly of ANNs
Performance Analysis of Open Source Machine Learning Frameworks for Various Parameters in Single-Threaded and Multi-Threaded Modes
The basic features of some of the most versatile and popular open source
frameworks for machine learning (TensorFlow, Deep Learning4j, and H2O) are
considered and compared. Their comparative analysis was performed and
conclusions were made as to the advantages and disadvantages of these
platforms. The performance tests for the de facto standard MNIST data set were
carried out on H2O framework for deep learning algorithms designed for CPU and
GPU platforms for single-threaded and multithreaded modes of operation Also, we
present the results of testing neural networks architectures on H2O platform
for various activation functions, stopping metrics, and other parameters of
machine learning algorithm. It was demonstrated for the use case of MNIST
database of handwritten digits in single-threaded mode that blind selection of
these parameters can hugely increase (by 2-3 orders) the runtime without the
significant increase of precision. This result can have crucial influence for
optimization of available and new machine learning methods, especially for
image recognition problems.Comment: 15 pages, 11 figures, 4 tables; this paper summarizes the activities
which were started recently and described shortly in the previous conference
presentations arXiv:1706.02248 and arXiv:1707.04940; it is accepted for
Springer book series "Advances in Intelligent Systems and Computing
Comparative Analysis of Open Source Frameworks for Machine Learning with Use Case in Single-Threaded and Multi-Threaded Modes
The basic features of some of the most versatile and popular open source
frameworks for machine learning (TensorFlow, Deep Learning4j, and H2O) are
considered and compared. Their comparative analysis was performed and
conclusions were made as to the advantages and disadvantages of these
platforms. The performance tests for the de facto standard MNIST data set were
carried out on H2O framework for deep learning algorithms designed for CPU and
GPU platforms for single-threaded and multithreaded modes of operation.Comment: 4 pages, 6 figures, 4 tables; XIIth International Scientific and
Technical Conference on Computer Sciences and Information Technologies (CSIT
2017), Lviv, Ukrain
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