Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

A novel bearing fault diagnosis model integrated permutation entropy, ensemble empirical mode decomposition and optimized SVM

A support vector machine–firefly algorithm-based model for global solar radiation prediction

Multi-fault diagnosis for rolling element bearings based on ensemble empirical mode decomposition and optimized support vector machines

A new hybrid support vector machine–wavelet transform approach for estimation of horizontal global solar radiation

Based on the Gabor transform, we propose a unified viewpoint for fingerprint image representation and recognition. In a local fingerprint image, we demonstrate that inter-ridge distances and ridge directions can be obtained by the principal Gabor basis function (GBF) directly. In addition, according to the locations of principal GBF on the spatial-frequency plane, the similarities of two local ridge structures can be measured easily. As compared with the global Gabor-based approach, the proposed method not only saves memory space, but also reaches a higher recognition rate.

http://ieeexplore.ieee.org/iel5/7375/20016/00925416.pdf

Fingerprint recognition using principal Gabor basis function

This paper proposes using the inter-cluster distance between class means in the feature space to help choose parameters for a kernel function when training a support vector machine (SVM). With the proposed method, the square values of the distance between the two class means of the training data in different feature spaces are calculated. These values are used as the indexes of data separation in the feature space. The experiment results show that the proposed method can choose the parameters close to the best ones. As a result, fewer possible values of the kernel parameters are required to be tested when training an SVM, and thus the training time of total training process can be significantly shortened.

Choosing the Kernel parameters of Support Vector Machines According to the Inter-cluster Distance

This paper presents a problem independent weighting strategy for weighted support vector machines (SVMs). SVMs can be applied with a weighting to each training vector to reflect the importance of different classes or training samples. Weightings are often assigned to the two classes inversely proportional to the sample count of each class, or according to a priori knowledge. Such a strategy can be applied to skewed data sets to balance the importance, error contribution and cost between the two classes. In this paper we propose a strategy to give each training pattern a weighting according to their distances to the classifier. The strategy regards the importance of the training patterns to the training process but not the importance of the data to the problem, thus it is suitable for general SVM applications. Experiments show that the performance of the proposed method is competitive to standard SVM while the training processes are even sped up.

A weighting initialization strategy for weighted support vector machines

Determining the kernel and error penalty parameters for support vector machines (SVMs) is very problem-dependent in practice. This paper proposes using a cluster validation index in the feature space to help choose parameters for training 2-norm soft margin support vector machines. With the proposed method, the kernel parameters and the penalty parameter of the error term in the 2-norm soft margin SVM are considered to be the parameters of an alternative kernel for a hard margin SVM. Thus the values of cluster validation index can be calculated in the feature spaces which are defined by the kernels with the parameters. The cluster validation index shows whether the data are well-separated in a feature space, so it can be used to determine whether a combination of the kernel parameters leads to a feature space in which the data are easy to be classified. It guides the search of parameters toward a good testing accuracy, so the search range of the parameters is confined to a small region, and the parameters selecting time of the SVM training process can be shortened.

Choosing the Parameters of 2-norm Soft Margin Support Vector Machines According to the Cluster Validity

http://ntur.lib.ntu.edu.tw/bitstream/246246/142303/1/38.pdf

Choosing the kernel parameters for support vector machines by the inter-cluster distance in the feature space

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