机器学习的研究

机器学习已经在许多方面取得了进步。这篇文章总结了其中的四个方面，并且讨论了当前一些公开的问题。四个方面的问题是 (1) 通过对多分类器集成的研究来提高分类的精确度。 (2)用按比例增加的方法负责研究算法 (3) 强化学习, 以及 (4) 复杂随机模型的学习。
过去五年在机器学习的研究方面已经取得了惊人的发展。导致这种发展的原因有许多：首先，基于符号机器学习的单独研究群体，计算的学习理论，神经网络，统计学 , 和模式识别都逐个被发现并且共同起作用。第二，机器学习技术正适合于解决新问题，包括数据库技术的发现、语言处理、机器人控制、和最佳化配置，还有许多传统问题比如语音识别、外观识别、笔迹识别、医学数据分析，以及游戏竞技。
在这篇文章中，我选择了四个有关机器学习的主题，近来在这些主题上都有许多探讨。写这篇文章的目的是展示机器学习在这些领域的进展以发展人工智能，和起草一些公开的研究问题。主要研究领域是(1) 多分类器集成,(2) 用按比例增加的方法负责研究算法(3)强化学习,以及(4)复杂随机模型的学习。(毕业设计)

Machine-learning research has been making great progress in many directions. This article summarizes four of these directions and discusses some current open problems. The four directions are (1) the improvement of classification accuracy by learning ensembles of classifiers, (2) methods for scaling up supervised learning algorithms, (3) reinforcement learning, and (4) the learning of complex stochastic models.
The last five years have seen an explosion in machine-learning research. This explosion has many causes: First, separate research communities in symbolic machine learning, computation learning theory, neural networks, statistics, and pattern recognition have discovered one another and begun to work together. Second, machine-learning techniques are being applied to new kinds of problem, including knowledge discovery in databases, language processing, robot control, and combinatorial optimization, as well as to more traditional problems such as speech recognition, face recognition, handwriting recognition, medical data analysis, and game playing.