Modern information society is facing the challenge of handling massive volume of online documents, news, intelligence reports, and so on. How to use the information accurately and in a timely manner becomes a major co...
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Empirical research in learning algorithms for classification tasks generally requires the use of significance tests. The quality of a test is typically judged on Type I error (how often the test indicates a difference...
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Person re-identification technology is an important research direction in the field of computer vision and intelligent transportation. Deep learning algorithms have better performance than traditional feature extracti...
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This paper presents a technique for evaluating the degree of correctness of structural models produced by Bayesian network learning algorithms. In this method, (1) Bayesian networks are generated pseudo-randomly using...
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ISBN:
(纸本)1577352882
This paper presents a technique for evaluating the degree of correctness of structural models produced by Bayesian network learning algorithms. In this method, (1) Bayesian networks are generated pseudo-randomly using a chosen model distribution;(2) data sets of various sizes are produced using the generated networks;(3) the data sets are passed to learning algorithms;and (4) the network structures output by the learning algorithms are compared to the original networks. In the past, similar methods have used individually hand-selected networks rather than generating large families of networks and have focused on data sets with a large number of cases. Sample results on several search-and-score algorithms are shown, and we discuss the use of domain-specific simulators for generating data which may be used to better evaluate the causal learning.
For any AC0 function f of n bits, there is a polynomial p such that any p(log n)-wzse decomposable distribution "fools" f. In other words, f cannot distinguish between the pseudorandom strings in the distrib...
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Advance perception and treatment of oral cancer are crucial for boosting survival chances. There are many deep learning techniques like Convolution Neural Network (CNN), Support vector Machine (SVM), and Region-Based ...
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Deep learning is a subset of machine learning that encompasses a variety of neural network architectures used to perform diverse computer vision tasks such as medical image classification and segmentation, which are t...
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Finite-element analysis of dynamic structures results in a finite number of mode shapes with corresponding frequencies and mode-shape scale factors. Most structural designs for spacecraft require the lowest frequencie...
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According to the principles of security management, intuitive scientificity, and scalability, an information security system architecture based on representation and metric deep learning algorithms was designed. Two k...
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Federated learning is a distributed machine learning approach that utilizes multiple devices' coordination for training purposes. Nevertheless, distributed computing tends to result in slow training. It is importa...
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