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

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

BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

“Bioinformatics” 특집을 내면서

Power Aware Routing in Mobile Ad Hoc Networks

This paper informs a statistical readership about Artificial Neural Networks (ANNs), points out some of the links with statistical methodology and encourages cross-disciplinary research in the directions most likely to bear fruit. The areas of statistical interest are briefly outlined, and a series of examples indicates the flavor of ANN models. We then treat various topics in more depth. In each case, we describe the neural network architectures and training rules and provide a statistical commentary. The topics treated in this way are perceptrons (from single-unit to multilayer versions), Hopfield-type recurrent networks (including probabilistic versions strongly related to statistical physics and Gibbs distributions) and associative memory networks trained by so-called unsupervised learning rules. Perceptrons are shown to have strong associations with discriminant analysis and regression, and unsupervized networks with cluster analysis. The paper concludes with some thoughts on the future of the interface between neural networks and statistics.

/pdf/neural-networks-a-review-from-a-statistical-perspective-1re2v41n6l.pdf

Neural Networks: A Review from a Statistical Perspective

In surveying current approaches to distributed shared memory computing, the authors find that the reduced cost of parallel software development will help make the DSM paradigm a viable solution to large scale, high performance computing.

/pdf/distributed-shared-memory-concepts-and-systems-5apsq9y5da.pdf

Distributed shared memory: concepts and systems

Extracting and processing information from Web pages is an important task in many areas like constructing search engines, information retrieval, and data mining from the Web. A common approach in the extraction process is to represent a page as a "bag of words" and then to perform additional processing on such a flat representation. We propose a new, hierarchical representation that includes browser screen coordinates for every HTML object in a page. Using visual information one is able to define heuristics for the recognition of common page areas such as header, left and right menu, footer and center of a page. We show in initial experiments that using our heuristics defined objects are recognized properly in 73% of cases. Finally, we show that a Naive Bayes classifier, taking into account the proposed representation, clearly outperforms the same classifier using only information about the content of documents.

Recognition of common areas in a Web page using visual information: a possible application in a page classification

Courses in Computer Architecture and Organization are regularly included in Computer Engineering curricula. These courses are usually organized in such a way that students obtain not only a purely theoretical experience, but also a practical understanding of the topics lectured. This practical work is usually done in a laboratory using simulators of computer systems. Since the open literature contains a variety of simulators being used for such purposes, this paper attempts to give a survey of simulators suitable for teaching courses in computer architecture and organization, to establish the evaluation criteria and to evaluate selected simulators according to these criteria.

A Survey and Evaluation of Simulators Suitable for Teaching Courses in Computer Architecture and Organization

An area of intensive research under the umbrella of the Internet of Things (IoT) has resulted in intensive proliferation of globally deployed sensor devices that provide a basis for the development of different use-case applications working with real-time data and demanding a rich user interface. Overcoming the lack of the standard HTML platform, HTML5 specifications WebSocket and Canvas graphics strongly supported the development of rich real-time applications. Such support has been offered by browser plug-ins such as Adobe Flash and Microsoft Silverlight for years. In order to provide a deep insight into IoT Web application performance, we implemented two test applications. In the first application, we measured latencies induced by different communication protocols and message encodings, as well as graphics rendering performance, while comparing the performance of different Web platform implementations. In the second application, we compared Web performance of IoT messaging protocols such as MQTT, AMQP, XMPP, and DDS by measuring the latency of sensor data message delivery and the message throughput rate. Our tests have shown that although Adobe Flash has the best performance at the moment, HTML5 platform is also very capable of running real-time IoT Web applications, whereas Microsoft Silverlight is noticeably behind both platforms. On the other hand, MQTT is the most appropriate messaging protocol for a wide set of IoT Web applications. However, IoT application developers should be aware of certain MQTT message broker implementation shortcomings that could prevent the usage of this protocol.

Web Performance Evaluation for Internet of Things Applications

An algebraic deterministic theory of neural nets pattern recognition and neuro engineering self-organizing neural network architectures for adaptive pattern recognition and robotics neural network applications to speech signal/image processing and underatanding with neural nwtworks networks for learning - a view from the theory of approximations of functions analog electronic neural networks for pattern recognition applications VLSI architectures for neural networks mapping the neural networks on the honeycomb architecture.

Veljko Milutinovic

Papers

Distributed shared memory: concepts and systems

Recognition of common areas in a Web page using visual information: a possible application in a page classification

A Survey and Evaluation of Simulators Suitable for Teaching Courses in Computer Architecture and Organization

Web Performance Evaluation for Internet of Things Applications

Neural Networks: Concepts, Applications, and Implementations