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

The previously known works studying the learning performance of multi-classification algorithm are usually based on supervised samples, but large amount of data generated in real-life is usually unlabeled. This paper introduces a novel Laplacian multi-classification support vector classification and regression (LMSVCR) algorithm for the case of semi-supervised learning. We first establish the fast learning rate of LMSVCR algorithm with semi-supervised multi-classification samples, and prove that LMSVCR algorithm with semi-supervised multi-classification samples is consistent. We show the numerical investigation on the learning performance of LMSVCR algorithm. The experimental studies indicate that the proposed LMSVCR algorithm has better learning performance in terms of prediction accuracy, sampling and training total time than other semi-supervised multi-classification algorithms.

LMSVCR: novel effective method of semi-supervised multi-classification

Introduction to multimodal interface for human-machine communication

A improved swarm optimization method based on particle swarm optimization (PSO) and simplified swarm optimization (SSO) is proposed to adjust the weight in artificial neural network. This method is a modification of traditional PSO and SSO, and combines them to a new optimization method (PSOSSO for short). The proposed method overcomes some of the drawbacks of SSO and improves its ability to train the weight of ANN. In the experiments, the PSOSSO is employed to train fuzzy wavelet neural network (FWNN) forecasting model to predict the prices of Hong Kong Hang Seng Index. The experimental results present that the PSOSSO is more efficient than traditional PSO and SSO methods.

A hybrid swarm optimization for neural network training with application in stock price forecasting

In this paper we proposed a novel supervised dimensionality reduction method, named Discriminant Isometric projection. The aim is to compact the data points from the same cluster on high-dimension manifold to make them closer in the low-dimension space, and to make the ones from the different cluster further, which is beneficial to preserve the homogeneous characteristics for classification. We compared our method with other three methods for dimensionality reduction over the ORL face dataset and experiments show that Discriminant Isometric projection produces stable performance and good precision.

Discriminant Isomap projection

In this paper, a novel approach to feature extraction with wavelet and fractal theories is presented as a powerful technique in pattern recognition The motivation behind using fractal transformation is to develop a high-speed feature extraction technique A multiresolution family of the wavelets is also used to compute information conserving micro-features In this study, a new fractal feature is reported We employed a central projection method to reduce the dimensionality of the original input pattern, and a wavelet transform technique to convert the derived pattern into a set of subpatterns, from which the fractal dimensions can readily be computed The new feature is a measurement of the fractal dimension, which is an important characteristic that contains information about the geometrical structure This new scheme includes utilizing the central projection transformation to describe the shape, the wavelet transformation to aid the boundary identification, and the fractal features to enhance image discrimination The proposed method reduces the dimensionality of a 2-D pattern by way of a central projection approach, and thereafter, performs Daubechies' wavelet transform on the derived 1-D pattern to generate a set of wavelet transform subpatterns, namely, curves that are non-self-intersecting Further from the resulting non-self-intersecting curves, the divider dimensions are computed with a modified box-counting approach These divider dimensions constitute a new feature vector for the original 2-D pattern, defined over the curve's fractal dimensions We have conducted several experiments in which a set of printed Chinese characters, English letters of varying fonts and other images were classified Based on the formulation of our new feature vector, the experiments have satisfying results

Yuan Yan Tang

Papers

LMSVCR: novel effective method of semi-supervised multi-classification

Introduction to multimodal interface for human-machine communication

A hybrid swarm optimization for neural network training with application in stock price forecasting

Discriminant Isomap projection

A combination of fractal and wavelet for feature extraction