Machine Learning

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1. Introduction Humans can expand their knowledge to adapt the changing environment. To do that they must “learn”. Learning can be simply defined as the acquisition of knowledge or skills through study, experience, or being taught. Although learning is an easy task for most of the people, to acquire new knowledge or skills from data is too hard and complicated for machines. Moreover, the intelligence level of a machine is directly relevant to its learning capability. The study of machine learning tries to deal with this complicated task. In other words, machine learning is the branch of artificial intelligence that tries to find an answer to this question: how to make computer learn? When we say that the machine learns, we mean that the machine is able to make predictions from examples of desired behavior or past observations and information. More formal definition of machine learning by Tom Mitchell is 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 in T, as measured by P, improves with experience E. The definition also indicates the main goal of machine learning: the design of such programs 2. Taxonomy of Machine Learning Machine learning systems can be categorized according to many different criteria. We will discuss three criteria: Classification on the basis of the underlying learning strategies used, Classification on the basis of the representation of knowledge or skill acquired by the learner and Classification in terms of the application domain of the performance system for which knowledge is acquired. There are two main types of machine learning system according the underlying learning... ... middle of paper ... ...d it can learn the face of him. In the next time the system will be able to recognize and categorize this person. References Tom, M. (1997). Machibe Learning. Machine Learning, Tom Mitchell, McGraw Hill, 1997: McGraw Hill. Mitchell, T. M. (2006). The Discipline of Machine Learning. Machine Learning Department technical report CMU-ML-06-108, Carnegie Mellon University. Alpaydin, E. (2004). Introduction to Machine Learning. Massachusetts, USA: MIT Press. Taiwo Oladipupo Ayodele (2010). Types of Machine Learning Algorithms, New Advances in Machine Learning, Yagang Zhang (Ed.), ISBN: 978-953-307-034-6, InTech, Available from: 1. T. Mitchell, Generative and Discriminative Classifiers: Naive Bayes and Logistic Regression. Draft Version, 2005 download
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