内容简介：For this family of models, the research needs to have at hand a dataset with some observations and the labels/classes of the observations. For example, the observations could be images of animals and the labels the name of the animal (e.g. cat, dog etc).Th
For this family of models, the research needs to have at hand a dataset with some observations and the labels/classes of the observations. For example, the observations could be images of animals and the labels the name of the animal (e.g. cat, dog etc).
These models learn from the labeled dataset and then are used to predict future events. For the training procedure, the input is a known training data set with its corresponding labels, and the learning algorithm produces an inferred function to finally make predictions about some new unseen observations that one can give to the model. The model is able to provide targets for any new input after sufficient training. The learning algorithm can also compare its output with the correct intended output (ground truth label) and find errors in order to modify itself accordingly (e.g. via back-propagation).
Supervisedmodels can be further grouped into regression and classification cases :
- Classification : A classification problem is when the output variable is a category e.g. “disease” / “no disease”.
- Regression : A regression problem is when the output variable is a real continuous value e.g. stock price prediction
Some examples of models that belong to this family are the following: SVC, LDA, SVR, regression, random forests etc.
2.2 Unsupervised machine learning algorithms/methods
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UNIX编程环境
Brian W.Kernighan、Rob Pike / 陈向群/等 / 机械工业出版社 / 1999-10-1 / 24.00
本书对UNIX操作系统的编程环境做了详细而深入的讨论，内容包括UNIX的文件系统、Shell、过滤程序、I/O编程、系统调用等，并对UNIX中的程序开发方法做了有针对性的指导。本书内容深入浅出，实例丰富，无论是UNIX系统的初学者还是专业人员都可从本书受益。本书亦可作为大学生、研究生学习UNIX的教材。一起来看看 《UNIX编程环境》 这本书的介绍吧!
