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

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

物件導向軟體之架構(Object-Oriented Software Construction)探討

Cause, conseguenze e strategie di mitigazione Proponiamo il primo di una serie di articoli in cui affronteremo l’attuale problema dei mutamenti climatici. Presentiamo il documento redatto, votato e pubblicato dall’Ipcc - Comitato intergovernativo sui cambiamenti climatici - che illustra la sintesi delle ricerche svolte su questo tema rilevante.

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Climate Change 2007: The Physical Science Basis.

We give an efficient procedure for verifying that a finite-state concurrent system meets a specification expressed in a (propositional, branching-time) temporal logic. Our algorithm has complexity linear in both the size of the specification and the size of the global state graph for the concurrent system. We also show how this approach can be adapted to handle fairness. We argue that our technique can provide a practical alternative to manual proof construction or use of a mechanical theorem prover for verifying many finite-state concurrent systems. Experimental results show that state machines with several hundred states can be checked in a matter of seconds.

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Automatic verification of finite-state concurrent systems using temporal logic specifications

Recommender systems use algorithms to provide users with product or service recommendations. Recently, these systems have been using machine learning algorithms from the field of artificial intelligence. However, choosing a suitable machine learning algorithm for a recommender system is difficult because of the number of algorithms described in the literature. Researchers and practitioners developing recommender systems are left with little information about the current approaches in algorithm usage. Moreover, the development of recommender systems using machine learning algorithms often faces problems and raises questions that must be resolved. This paper presents a systematic review of the literature that analyzes the use of machine learning algorithms in recommender systems and identifies new research opportunities. The goals of this study are to (i) identify trends in the use or research of machine learning algorithms in recommender systems; (ii) identify open questions in the use or research of machine learning algorithms; and (iii) assist new researchers to position new research activity in this domain appropriately. The results of this study identify existing classes of recommender systems, characterize adopted machine learning approaches, discuss the use of big data technologies, identify types of machine learning algorithms and their application domains, and analyzes both main and alternative performance metrics.

The use of machine learning algorithms in recommender systems: A systematic review

Recommender systems use algorithms to provide users with product or service recommendations. Recently, these systems have been using machine learning algorithms from the field of artificial intelligence. However, choosing a suitable machine learning algorithm for a recommender system is difficult because of the number of algorithms described in the literature. Researchers and practitioners developing recommender systems are left with little information about the current approaches in algorithm usage. Moreover, the development of a recommender system using a machine learning algorithm often has problems and open questions that must be evaluated, so software engineers know where to focus research efforts. This paper presents a systematic review of the literature that analyzes the use of machine learning algorithms in recommender systems and identifies research opportunities for software engineering research. The study concludes that Bayesian and decision tree algorithms are widely used in recommender systems because of their relative simplicity, and that requirement and design phases of recommender system development appear to offer opportunities for further research.

The Use of Machine Learning Algorithms in Recommender Systems: A Systematic Review

This paper introduces S.P.L.O.T., a Web-based reasoning and configuration system for Software Product Lines (SPLs). The system benefits from mature logic-based reasoning techniques such as SAT solvers and binary decision diagrams to provide efficient reasoning and interactive configuration services to SPL researchers and practitioners. In addition, the system provides a feature model repository containing real and generated models to encourage knowledge sharing among researchers in the field.

S.P.L.O.T.: software product lines online tools

The production of error-free protocols or complex process interactions is essential to reliable communications. This paper presents techniques for both the detection of errors in protocols and for prevention of errors in their design. The methods have been used successfully to detect and correct errors in existing protocols. A technique based on a reachability analysis is described which detects errors m a design. This "perturbation technique" has been implemented and has successfully detected inconsistencies or errors in existing protocol designs including both X.21 and X.25. The types of errors handled are state deadlocks, unspecified receptions, nonexecutable interactions, and state smbiguities. These errors are discussed and their effects considered. An interactive design technique is then described that prevents design errors. The technique is based on a set of production rules which guarantee that complete reception capability is provided in the interacting processes. These rules have been implemented in the form of a tracking algorithm that prevents a designer from creating unspecified receptions and nonexecutable interactions and monitors for the presence of state deadlocks and ambiguities.

Towards Analyzing and Synthesizing Protocols

Feature modeling is used in generative programming and software product-line engineering to capture the common and variable properties of programs within an application domain. The translation of feature models to propositional logics enabled the use of reasoning systems, such as BDD engines, for the analysis and transformation of such models and interactive configurations. Unfortunately, the size of a BDD structure is highly sensitive to the variable ordering used in its construction and an inappropriately chosen ordering may prevent the translation of a feature model into a BDD representation of a tractable size. Finding an optimal order is NP-hard and has for long been addressed by using heuristics.We review existing general heuristics and heuristics from the hardware circuits domain and experimentally show that they are not effective in reducing the size of BDDs produced from feature models. Based on that analysis we introduce two new heuristics for compiling feature models to BDDs. We demonstrate the effectiveness of these heuristics using publicly available and automatically generated models. Our results are directly applicable in construction of feature modeling tools.

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Donald D. Cowan

Papers

The use of machine learning algorithms in recommender systems: A systematic review

The Use of Machine Learning Algorithms in Recommender Systems: A Systematic Review

S.P.L.O.T.: software product lines online tools

Towards Analyzing and Synthesizing Protocols

Efficient compilation techniques for large scale feature models