机器学习

机器学习是通过使用示例数据或过往经验来编程计算机，以优化某一性能标准。我们有一个已经定义了部分参数的模型，而学习就是执行一个计算机程序，利用训练数据或过往经验来优化模型的参数。该模型可以是预测性的，用于对未来进行预测；也可以是描述性的，用于从数据中获取知识；或者兼具两者的功能。

亚瑟·塞缪尔（Arthur Samuel）是计算机游戏和人工智能领域的早期美国先驱，他于1959年在IBM公司创造了“机器学习”一词。他将机器学习定义为“让计算机无需明确编程即可学习的研究领域”。然而，目前对于机器学习并没有一个普遍接受的定义，不同的作者对这个术语有不同的解释。

学习的定义

定义

如果一个计算机程序在任务 T 上的性能，通过性能度量 P 来衡量，随着经验 E 的增加而提高，那么就称该程序从经验 E 中学习。

示例

i) 手写识别学习问题

• 任务 T: 识别和分类图像中的手写文字
• 性能 P: 正确分类单词的百分比
• 训练经验 E: 带有已知分类的手写单词数据集

ii) 机器人驾驶学习问题

• 任务 T: 使用视觉传感器在高速公路上驾驶
• 性能度量 P: 在出错前的平均行驶距离
• 训练经验 E: 观察人类驾驶员时记录的一系列图像和转向指令

iii) 国际象棋学习问题

• 任务 T: 下国际象棋
• 性能度量 P: 赢得对手游戏的百分比
• 训练经验 E: 与自身进行练习赛

学习程序

从经验中学习的计算机程序被称为机器学习程序，或简称学习程序。这样的程序有时也称为学习器。

Machine learning is programming computers to optimize a performance criterion using example
data or past experience. We have a model defined up to some parameters, and learning is the
execution of a computer program to optimize the parameters of the model using the training data or
past experience. The model may be predictive to make predictions in the future, or descriptive to gain
knowledge from data, or both.
Arthur Samuel, an early American leader in the field of computer gaming and artificial intelligence,
coined the term “Machine Learning” in 1959 while at IBM. He defined machine learning as “the field of
study that gives computers the ability to learn without being explicitly programmed.” However, there is
no universally accepted definition for machine learning. Different authors define the term differently.
Definition of learning
Definition
A computer program is said to learn from experience E with respect to some class of tasks T and
performance measure P, if its performance at tasks T, as measured by P, improves with experience E.
Examples
i) Handwriting recognition learning problem
• Task T: Recognising and classifying handwritten words within images
• Performance P: Percent of words correctly classified
• Training experience E: A dataset of handwritten words with given classifications
ii) A robot driving learning problem
• Task T: Driving on highways using vision sensors
• Performance measure P: Average distance traveled before an error
• training experience: A sequence of images and steering commands recorded while
observing a human driver
iii) A chess learning problem
• Task T: Playing chess
• Performance measure P: Percent of games won against opponents
• Training experience E: Playing practice games against itself
Definition
A computer program which learns from experience is called a machine learning program or
simply a learning program. Such a program is sometimes also referred to as a learner.