机器学习的一些新方法

N. Findler
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引用次数: 13

摘要

在一个复杂的计算机程序中建立了五种不同但相互关联的学习模式。这些模型结合了在算法和启发式基础上优化响应模式的机制;在不同层次上进行抽象;产生价值判断;识别、修改、存储和检索几何图形;总的来说,展示了智能行为的许多方面。教师和学习者都在机器中被模拟。在一个模型中,程序遵循一种新的学习过程,生成自己的策略,并根据经验对其进行改进。这种方法能使学习者超越老师的演奏质量。有人建议,项目中采用的方法和技术可能有助于将一些可简化为模式识别的问题解决活动机械化,例如气象预报、医疗诊断、交通管制等。没有人刻意模仿人类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Some New Approaches to Machine Learning
Five different but interrelated models of learning have been established within a complex computer program. These models incorporate mechanisms that optimize response patterns on algorithmic and heuristic bases; make abstractions at different levels; produce value judgements; recognize, modify, store, and retrieve geometrical patterns; and exhibit, in general, many aspects of intelligent behavior. Both the teacher and the learner are simulated in the machine. In one model, the program follows a qualitatively new kind of learning process in generating its own strategy and improving it on the basis of experience. The method enables the learner to exceed the playing quality of the teacher. It is suggested that the methods and techniques employed in the project may be useful in mechanizing some problem-solving activities that can be reduced to pattern recognition, such as meteorological forecasting, medical diagnosis, traffic control, and so on. No deliberate attempt has been made to imitate humans.
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