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

Data Mining - Concepts and Techniques.

Data Mining Practical Machine Learning Tools and Techniques

I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

How has Japan become a major economic power, a world leader in the automotive and electronics industries? What is the secret of their success? The consensus has been that, though the Japanese are not particularly innovative, they are exceptionally skilful at imitation, at improving products that already exist. But now two leading Japanese business experts, Ikujiro Nonaka and Hiro Takeuchi, turn this conventional wisdom on its head: Japanese firms are successful, they contend, precisely because they are innovative, because they create new knowledge and use it to produce successful products and technologies. Examining case studies drawn from such firms as Honda, Canon, Matsushita, NEC, 3M, GE, and the U.S. Marines, this book reveals how Japanese companies translate tacit to explicit knowledge and use it to produce new processes, products, and services.

The knowledge-creating company : how Japanese companies create the dynamics of innovation

Decision support in bioinformatics research for virtual plants

This paper proposes a spatial index structure based on a new space-partitioning method. Previous research proposed various high dimensional index structures. However, when dimensionality becomes high, the effectiveness of the spatial index structure disappears. This problem is called the “curse of dimensionality”. This paper focuses on the fact that the volume of high dimensional space is mostly occupied by its surface and then proposes a new surface index structure. The utility of this new surface spatial index structure is illustrated through a series of experiments.

Surface Spatial Index Structure of High-Dimensional Space

Classification problem widely exists in real-world applications. Unfortunately, data quality is the main challenge of the classification models, especially when the data includes irrelevant and redundant features. Feature selection (FS) is an effective preprocessing technique to enhance the quality of the data. For this, an integration of information theory and fuzzy sets introduced powerful measures, such as fuzzy information measures, to develop many feature selection methods. However, estimating fuzzy information measures is not only costly in the space and runtime but also may be affected by the bias between the certainty and uncertainty regions. This paper proposes a novel instance selection based on uncertainty region (ISUR) to overcome these limitations. Then, a state-of-the-art FS method, called fuzzy joint mutual information (FJMI), has been adapted to design an effective FS method, called fuzzy joint mutual information feature selection based on uncertainty region (FJMIUR). The proposed method consists of two processes: instance selection and feature selection. The former selects the uncertainty region that improves the estimation of fuzzy information measures and reduces the consumed cost while the latter selects the most significant features. Using 20 real-world classification datasets, comparative experiments, including well-known and state-of-the-art FS methods, were conducted to evaluate the effectiveness of FJMIUR. The results show the outperformance of FJMIUR in most cases according to six classification measures, average percentage of selected features, space, and runtime. 

Effective fuzzy joint mutual information feature selection based on uncertainty region for classification problem

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Integrating for More Complete Sports Video Summarization

Those pre-miRNAs with multiple loops are usually excluded in the most existing prediction methods. But as more and more miRNA have been identified, amount of miRNA precursor with multiple loops have been found. Therefore, determining how to effectively predict real pre-miRNA with multiple loops from those large of pseudo pre-miRNAs with multiple loops is an imperative problem. Some features of main branch are extracted to describe pre-miRNA intrinsic features, and SVM classifier is implemented to recognize real pre-miRNA with multiple stem-loops. Training and testing on dataset from miRBase12.0, SVM classifier achieves sensitivity of 75.76% and specificity of 95.16% on human test set, and when being applied to pre-miRNAs of all other species, it correctly identifies 86.71% of them. The proposed method in this work provides a powerful predicting method to recognize the real pre-miRNA with multiple stem-loops.

Yi-Ping Phoebe Chen

Papers

Decision support in bioinformatics research for virtual plants

Surface Spatial Index Structure of High-Dimensional Space

Effective fuzzy joint mutual information feature selection based on uncertainty region for classification problem

Integrating for More Complete Sports Video Summarization

Identifying multiple stem-loops pre-miRNA using support vector machine