Machine Learning Tutorial
By Wei-Lun Chao
In the topics of face recognition, face detection, and facial age estimation, machine learning plays an important role and is served as the fundamental technique in many existing literatures.
For example, in face recognition, many researchers focus on using dimensionality reduction techniques for extracting personal features. The most well-known ones are (1) eigenfaces , which is based on principal component analysis (PCA,) and (2) fisherfaces , which is based on linear discriminant analysis (LDA).
In face detection, the popular and efficient technique based on Adaboost cascade structure , which drastically reduces the detection time while maintains comparable accuracy, has made itself available in practical usage. Based on our knowledge, this technique is the basis of automatic face focusing in digital cameras. Machine learning techniques are also widely used in facial age estimation to extract the hardly found features and to build the mapping from the facial features to the predicted age.
Although machine learning is not the only method in pattern recognition (for example, there are still many researches aiming to extract useful features through image and video analysis), it could provide some theoretical analysis and practical guidelines to refine and improve the recognition performance. In addition, with the fast development of technology and the burst usage of Internet, now people can easily take, make, and access lots of digital photos and videos either by their own digital cameras or from popular on-line photo and video collections such as Flicker , Facebook , and Youtube . Based on the large amount of available data and the intrinsic ability to learn knowledge from data, we believe that the machine learning techniques will attract much more attention in pattern recognition, data mining, and information retrieval.
In this tutorial, a brief but broad overview of machine learning is given, both in theoretical and practical aspects. In Section 2, we describe what machine learning is and its availability. In Section 3, the basic concepts of machine learning are presented, including categorization and learning criteria. The principles and effects about the learning performance are discussed in Section 4, and several supervised and unsupervised learning algorithms are introduced in Sections 5 and 6. In Section 7, a general framework of pattern recognition based on machine learning technique is provided. Finally, in Section 8, we give a conclusion.