数据挖掘(Data mining)一般是指从大量的数据中自动搜索隐藏于其中的有着特殊关系性的信息和知识的过程。

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摘要: 人工智能并没有一个统一的定义,但若一个计算机系统能做人需要智能才能做的事,一般便认为这样的计算机系统具有人工智能。因此,人工智能被广泛应用于许多需要人类智能的领域,如法律、医疗、金融、电子商务等,其中法律是当前的一个重要应用领域。因此,文中主要从立法(人工智能系统辅助立法以及立法监管人工智能系统,特别是自主驾驶汽车)、知法守法(法律信息的检索、法律文书的生成和审核)、司法(证据收集、法律推理以及在线纠纷解决)等方面综述了人工智能和法律结合的研究现状以及发展趋势,希望能引导更多人投入这个研究领域。

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In this paper, we review data mining approaches for health applications. Our focus is on hardware-centric approaches. Modern computers consist of multiple processors, each equipped with multiple cores, each with a set of arithmetic/logical units. Thus, a modern computer may be composed of several thousand units capable of doing arithmetic operations like addition and multiplication. Graphic processors, in addition may offer some thousand such units. In both cases, single instruction multiple data and multiple instruction multiple data parallelism must be exploited. We review the principles of algorithms which exploit this parallelism and focus also on the memory issues when multiple processing units access main memory through caches. This is important for many applications of health, such as ECG, EEG, CT, SPECT, fMRI, DTI, ultrasound, microscopy, dermascopy, etc.

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