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

In this paper we present generic database model tools for L-systems (DBM-L). DBM-L allows representation of L-Systems objects as database structures by a generic automatic translation process. This research contributes the idea of pre-computing recursive structures data into derived attributes using compiler generation. In this paper we supplied a method to allow a correspondence biologist's terms and compiler generated terms and to apply a biological friendly computing environment. We also present a visualization tool (VT) to representing the different data structures. The whole DBM-L provides a valuable package for biological scientific database management.

Querying recursive structures without recursive queries

Many classification methods have been proposed to find patterns in text documents. However, according to Occam?s razor principle, "the explanation of any phenomenon should make as few assumptions as possible", short patterns usually have more explainable and meaningful for classifying text documents. In this paper, we propose a depth-first pattern generation algorithm, which can find out short patterns from text document more effectively, comparing with breadth-first algorithm.

http://dro.deakin.edu.au/eserv/DU:30006067/chen-findingshortpatterns-2006.pdf

Finding Short Patterns to Classify Text Documents

Non-coding RNA (ncRNA) is involved in many biological processes and diseases in all species Many ncRNA datasets exist that provide a sequential representation of data that best suits biomedical purposes However, for ncRNA identification and analysis, statistical learning methods require hidden numerical features from the data The extraction of hidden features, their analysis, and usage of a suitable set of features is crucial towards any statistical learning methods performance Furthermore, a wealth of sequence intrinsic features has been proposed for ncRNA identification Therefore, a systematic review and selection of these features are warranted First, fasta format sequence datasets are generated from RNACentral representing many ncRNA types across a number of species Next, a features dataset is created per fasta dataset consisting of 17 most frequently reported sequence intrinsic features The features dataset is available from the FexRNA platform developed as part of this work In addition, the features datasets are explored and analysed in terms of statistical information, univariate and bivariate analysis For the feature selection (FS), a two-fold hierarchal FS framework based on majority voting and correlation is proposed and evaluated Therefore, the FexRNA platform provides a useful platform for information about ncRNA features datasets, features analysis, and selection

FexRNA: Exploratory Data Analysis and Feature Selection of non-coding RNA.

PaMeCo join: A parallel main memory compact hash join

Paediatric asthma represents a significant public health problem. To date, clinical data sets have typically been examined using traditional data analysis techniques. While such traditional statistical methods are invariably widespread, large volumes of data may overwhelm such approaches. The new generation of knowledge discovery techniques may therefore be a more appropriate means of analysis. The primary purpose of this study was to investigate an asthma data set, with the application of various data mining techniques for knowledge discovery. The current study utilises data from an asthma data set (n ≈ 17000). The findings revealed a number of factors and patterns of interest.

Yi-Ping Phoebe Chen

Papers

Querying recursive structures without recursive queries

Finding Short Patterns to Classify Text Documents

FexRNA: Exploratory Data Analysis and Feature Selection of non-coding RNA.

PaMeCo join: A parallel main memory compact hash join

Medical Knowledge Discovery from a Regional Asthma Dataset