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

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

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Convex Analysisの二,三の進展について

Assay for lipid peroxides in animal tissues by thiobarbituric acid reaction

Recently, researchers have discovered that there are non-invasive ways to detect Diabetes Mellitus based on the analysis of human facial blocks Even though a few algorithms have been developed to detect Diabetes Mellitus based on facial feature analysis, Diabetes Mellitus detection remains a challenging problem In this paper, we propose two algorithms to detect Diabetes Mellitus through the analysis of facial texture features extracted by the Gabor filter: Simplified Patch Ordering and Improved Patch Ordering Firstly, four facial blocks are taken from a facial image to represent it Afterwards, we use a 2-D Gabor filter bank to extract texture features from the four facial blocks Finally, Simplified Patch Ordering and Improved Patch Ordering are applied to classify Diabetes Mellitus and Healthy samples Experimental results on a dataset show that Simplified Patch Ordering can classify Diabetes Mellitus and Healthy samples with an accuracy of 9583%, a sensitivity of 100%, and a specificity of 9167%, while Improved Patch Ordering can classify Diabetes Mellitus and Healthy samples with an accuracy of 9938%, a sensitivity of 100%, and a specificity of 9735%, both based on a combination of facial blocks

Simplified and Improved Patch Ordering for Diabetes Mellitus detection

Handwriting-based writer identification is a hot research filed in pattern recognition. Off-line text-independent writer identification still remains as a challenging problem because writing features can only be extracted from the handwriting images. As a result, plenty of dynamic writing information, which is very valuable for writer identification, is unavailable for off-line writer identification. This results in high error rate in off-line writer identification. In order to enhance the performance of off-line writer identification, a complex wavelet-based GGD method was presented in this paper. The novel method is based on our discovery that complex wavelet coefficients within each high-frequency sub-band of the handwritings satisfy GGD distribution. Our experiments show the new method, compared with the traditional wavelet-based GGD method, and our method achieves a better performance.

Handwriting-based writer identification with complex wavelet transform

Research has been active in the field of forgery detection, but relatively little work has been done on the detection of skilled forgeries. This paper presents a new feature extraction method based on the intensity of the coefficients of the Gabor transform. The new method first uses the multichannel Gabor transform, then the transformed image of Gabor was equally divided into some non-overlapping boxes, and the angle features of the position of the maxima intensity of the Gabor transform coefficients were extracted. Experiment results indicate that the proposed method enable to improve verification accuracy.

Offline signature verification based on the gabor transform

In this paper, a multi-resolution rigid-model-based global matching algorithm is employed to register tree structures of blood vessels extracted from retinal fundus images. To further improve alignment of the vessels, a local structure-deformed elastic matching algorithm is proposed to eliminate the existence of ‘ghost vessels' for accurate registration. The matching methods are tested on 268 pairs of retinal fundus images. Experiment results show that our global-to-local registration strategy is able to achieve an average centreline mapping errors of 1.85 pixels with average execution time of 207 seconds. The registration results have also been visually validated by corresponding fusion maps.

A global-to-local matching strategy for registering retinal fundus images

In this paper, we propose a Locality Preserving Nonnegative Matrix Factorization (LPNMF) method to discover the manifold structure embedded in high-dimensional face space that is applied for face recognition. It is done by incorporating locality preserving constraints inside the cost function of NMF, then a new decomposition of a face with locality preserving can be obtained. As a result, the proposed LPNMF method shares some properties with the Locality Preserving Projection (LPP) such that it can effectively discover the manifold structure embedded in a high-dimensional face space. Experimental results show that LPNMF provides a better representation and achieves higher recognition rates in face recognition.

Yuan Yan Tang

Papers

Simplified and Improved Patch Ordering for Diabetes Mellitus detection

Handwriting-based writer identification with complex wavelet transform

Offline signature verification based on the gabor transform

A global-to-local matching strategy for registering retinal fundus images

Locality preserving nonnegative matrix factorization with application to face recognition