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

The high-throughput experimental data from the new gene microarray technology has spurred numerous efforts to find effective ways of processing microarray data for revealing real biological relationships among genes. This work proposes an innovative data pre-processing approach to identify noise data in the data sets and eliminate or reduce the impact of the noise data on gene clustering, With the proposed algorithm, the pre-processed data sets make the clustering results stable across clustering algorithms with different similarity metrics, the important information of genes and features is kept, and the clustering quality is improved. The primary evaluation on real microarray data sets has shown the effectiveness of the proposed algorithm.

http://dro.deakin.edu.au/eserv/DU:30020774/hou-datapreprocessingpublished-2009.pdf

Data Pre-processing for More Effective Gene Clustering

Abstractive text summarization (ATS) often fails to capture salient information and preserve the original meaning of the content in the generated summaries due to a lack of background knowledge. We present a method to provide the topic information based on the background knowledge of documents to a deep learning-based summarization model. This method comprises a topic knowledge base (TKB) and convolutional sequence network-based text summarization model with knowledge-powered topic level attention (KTOPAS). TKB employs conceptualization to retrieve the semantic salient knowledge of documents and the knowledge-powered topic model (KPTopicM) to generate coherent and meaningful topic information by utilizing the knowledge that represents the documents well. KTOPAS obtains knowledge-powered topic information (also called topic knowledge) from TKB and incorporates the topic knowledge into the convolutional sequence network through a high-level topic level attention to resolve the existing issues in ATS. KTOPAS introduces a tri-attention channel to jointly learn the attention of the source elements over the summary elements, the source elements over topic knowledge, and topic knowledge over the summary elements to present the contextual alignment information from three aspects and combine them using the softmax function to generate the final probability distribution which enables the model to produce coherent, concise, and human-like summaries with word diversity. By conducting experiments on datasets, namely CNN/Daily Mail and Gigaword, the results show that our proposed method consistently outperforms the competing baselines. Moreover, TKB improves the effectiveness of the resulting summaries by providing topic knowledge to KTOPAS and demonstrates the quality of the proposed method.

Joint knowledge-powered topic level attention for a convolutional text summarization model

Biological Sequence Assembly and Alignment

The AMP-activated protein kinase (AMPK) acts as a metabolic master switch regulating several intracellular systems. The effect of AMPK on muscle cellular energy status makes this protein a promising pharmacological target for disease treatment. With increasingly available AMPK regulation data, it is critical to develop an efficient way to analyze the data since this assists in further understanding AMPK pathways. Bayesian networks can play an important role in expressing the dependency and causality in the data. This paper aims to analyse the regulation data using B-Course, a powerful analysis tool to exploit several theoretically elaborate results in the fields of Bayesian and causal modelling, and discover a certain type of multivariate probabilistic dependencies. The identified dependency models are easier to understand in comparison with the traditional frequent patterns.

Learning dependency model for AMP-activated protein kinase regulation

Due to the recent popularization of digital broadcasting systems, close-range broadcasting using continuous media data, i.e. audio and video, has attracted great attention. For example, in a drama, after a user watches interesting content such as a highlight scene, he/she will watch the main program continuously. In close-range broadcasting, the necessary bandwidth for continuously playing the two types of data increases. Conventional methods reduce the necessary bandwidth by producing an effective broadcast schedule for continuous media data. However, these methods do not consider the broadcast schedule for two types of continuous media data. When the server schedules two types of continuous media data, waiting time that occurs from finishing the highlight scene to starting the main scene, may increase. In this paper, we propose a scheduling method to reduce the waiting time for close-range broadcasting. In our proposed method, by dividing two types of data and producing an effective broadcast schedule considering the available bandwidth, we can reduce the waiting time.

/pdf/a-scheduling-method-to-reduce-waiting-time-for-close-range-cehyo4fbfd.pdf

Yi-Ping Phoebe Chen

Papers

Data Pre-processing for More Effective Gene Clustering

Joint knowledge-powered topic level attention for a convolutional text summarization model

Biological Sequence Assembly and Alignment

Learning dependency model for AMP-activated protein kinase regulation

A scheduling method to reduce waiting time for close-range broadcasting