The Internet of Things (IoT) is a network of interconnected devices that may be used to remotely detect, identify, and operate physical objects. IoT's qualities allow for the incorporation of the real world into a...
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This paper proposes a practical list of safety concerns and mitigation methods for visual deep learning algorithms. The growing success of deep learning algorithms in solving nonlinear and complex problems has recentl...
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The research on traditional lithium battery charging systems has problems such as model simplification, insufficient data, insufficient accuracy, and poor real-time performance. Simplified electrochemical models canno...
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Our research proposes a comprehensive approach to identify duplicate frames in digital videos. It integrates machine learning and signal processing techniques for effective identification. The process begins with pre-...
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Lifelong Machine learning, or LML, considers systems that can learn many tasks from one or more domains over its lifetime. The goal is to sequentially retain learned knowledge and to selectively transfer that knowledg...
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To improve the spectral efficiency in cognitive radio networks, it is essential for cognitive radio users to be equipped with intelligent learning capability. Many different learning methods have been applied in diffe...
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Information System security is becoming a critical issue today, given the large-scale use of the Internet, the diversity of storage and different means of exchanging information. Solutions developed based on signature...
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The idea of two-phase learning has been proposed here for effectively solving the learning problems in which training instances come in a two-stage way. Several two-phase learning algorithms based on the learning meth...
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The idea of two-phase learning has been proposed here for effectively solving the learning problems in which training instances come in a two-stage way. Several two-phase learning algorithms based on the learning method PRISM have also been proposed for inducing rules from training instances. These alternatives form a spectrum, showing achievement of the requirement of PRISM being heavily dependent on the spent computational cost in Phase 2.
作者：
Liu, JingLaw School
Xi'an Jiaotong University 28 Xianning West Road Shaanxi Province Xi'an China
After deep learning algorithms were proposed, artificial intelligence technology applications have achieved breakthrough development. Currently, the explosive growth of data provides sufficient nutrients for artificia...
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ISBN:
(纸本)9781510655133
After deep learning algorithms were proposed, artificial intelligence technology applications have achieved breakthrough development. Currently, the explosive growth of data provides sufficient nutrients for artificial intelligence, and deep learning algorithms have achieved breakthroughs in speech and visual recognition, making it possible for artificial intelligence industries to land and commercialize. With the continuous development of big data, deep learning and cloud computing, we can obtain more and richer data, develop more efficient algorithms, and have more powerful computing power, laying the foundation for another artificial intelligence research boom. However, with the application of AI-related technologies in industries such as information, social governance, and transportation, the problems and challenges of algorithmic collusion and algorithmic discrimination have gradually emerged. The operating principles of algorithms differ from the risk of algorithmic collusion that may result, and they also pose different degrees of regulatory challenges for antitrust enforcement. By understanding the data-driven competitive model of the market under the influence of algorithms, the efficient information interaction mechanism and the new features embodied in the evolution of machine-driven competition can prevent the breeding of technological monopolies. The impact of algorithms on the collusion problem has two dimensions: the first dimension is to change the environment of collusion;the second dimension is to be applied directly as a tool in the collusion process. This article attempts to analyze the challenges brought to modern market competition by exploring the state of artificial intelligence technology that causes data monopoly. Thus, In order to better regulate the evaluation and regulation of artificial intelligence algorithm collusion, this article proposes related solutions based on the Chinese perspective, including: 1) broaden the extension of the c
In this article we show how can be parallelized efficiently PAC-learning algorithms for some specifically represented concepts that cover a very wide class of concepts to be learned in spite of the difficulty of paral...
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ISBN:
(纸本)0889863490
In this article we show how can be parallelized efficiently PAC-learning algorithms for some specifically represented concepts that cover a very wide class of concepts to be learned in spite of the difficulty of parallelization of KDD based algorithms normally used in Data mining. Additionally we propose an alternative approach for doing KDD that makes a trade-off between performance and precision using parallel versions of PAC-learning algorithms for learning PAC-learnable concepts (concepts expressed in k-CNF and monotonie k-DNF, simple decision lists, equivalence query simulation using less examples, logic recursive programs, concepts with finite Vapnik-Chervonenkis dimension).
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