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

A 2001 IBM manifesto observed that a looming software complexity crisis -caused by applications and environments that number into the tens of millions of lines of code - threatened to halt progress in computing. The manifesto noted the almost impossible difficulty of managing current and planned computing systems, which require integrating several heterogeneous environments into corporate-wide computing systems that extend into the Internet. Autonomic computing, perhaps the most attractive approach to solving this problem, creates systems that can manage themselves when given high-level objectives from administrators. Systems manage themselves according to an administrator's goals. New components integrate as effortlessly as a new cell establishes itself in the human body. These ideas are not science fiction, but elements of the grand challenge to create self-managing computing systems.

The vision of autonomic computing

The term “Linked Data” refers to a set of best practices for publishing and connecting structured data on the Web. These best practices have been adopted by an increasing number of data providers over the last three years, leading to the creation of a global data space containing billions of assertions— the Web of Data. In this article, the authors present the concept and technical principles of Linked Data, and situate these within the broader context of related technological developments. They describe progress to date in publishing Linked Data on the Web, review applications that have been developed to exploit the Web of Data, and map out a research agenda for the Linked Data community as it moves forward.

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Linked Data - the story so far

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

The growing diffusion of portable devices enables users to benefit from anytime and anywhere impromptu collaboration. Appropriate policy models that take into account the dynamicity and heterogeneity of the new pervasive collaboration scenario are crucial to ensure secure sharing of information. Collaborating entities cannot be predetermined and resource availability frequently varies, even unpredictably, due to user/device mobility, thus complicating resource access control. Policies cannot be defined based on entity's identities/roles, as in traditional security solutions, or be specified a priori to face any operative run-time condition, and require continuous adjustments to adapt to the current situation. To address these issues this paper advocates the adoption of a semantic context-aware paradigm to policy specification. Context- awareness allows operations on resources to be controlled based on context visibility whereas semantic technologies allow the high-level description and reasoning about context/policies. The paper describes Proteus that, as a key feature, combines these two design guidelines to enable dynamic adaptation of policies depending on context changes. In particular, the paper shows how ontologies and logic programming rules can be used to leverage policy adaptation.

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Proteus: A Semantic Context-Aware Adaptive Policy Model

The use of description logics (DLs) in modeling various real-world domains, and reasoning about them, has well-known benefits. We believe that the same framework can be used for representing a user-centric view of usage contexts. A DL-reasoner can then be used for organizing context definitions, merging domain knowledge into these definitions, and performing recognition of contexts from sensor inputs. A pure DL-based approach, however, has certain limitations in a context environment; hence, a hybrid reasoning approach is proposed. To ensure syntactic and semantic interoperability, we adopt a semantic Web-compliant approach that uses the OWL-DL variant of the OWL Web ontology language. This allows us to aggregate various heterogeneous data sources, both directly in semantic Web formalisms, and indirectly via the use of a transformation framework capable of using data from legacy applications. Context and domain models, along with associated reasoners, are themselves context, user and/or organization-specific; hence multiple systems should be maintained and connected into a distributed architecture. In order to deploy this architecture in a mobile environment, an underlying SIP-based provisioning framework has been experimented with, enabling device, network and location transparency.

Contextualizing applications via semantic middleware

Entailment, as defined by RDF's model-theoretical semantics, is a basic requirement for processing RDF, and represents the kind of "semantic interoperability" that RDF-based systems have been anticipated to have to realize the vision of the "Semantic Web". In this paper we give some results in our investigation of a practical implementation of the entailment rules, based on the graph-walking query mechanism of the WILBUR RDF toolkit.

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Taking the RDF Model Theory Out for a Spin

The Semantic Web published as an article in Scientific American

A method of facilitating a service includes receiving a description of a service, and invoking the service based on the description. In this regard, the description comprises one having been generated based on an ontology including a set of classes, instances and associated properties for describing a user interface (UI) that includes one or more fields. The set of classes, instances and associated properties include a class for describing a field of the UI, and one or more properties for describing a name of the respective field and/or a UI widget for implementing the respective field. In this regard, the description includes a UI model of the service, where the UI model includes one or more of the classes, instances and associated properties of the ontology. Invoking the service, then, includes generating a UI based on the UI model.

Ora Lassila

Papers

Proteus: A Semantic Context-Aware Adaptive Policy Model

Contextualizing applications via semantic middleware

Taking the RDF Model Theory Out for a Spin

The Semantic Web published as an article in Scientific American

Personalized user interfaces for presentation-oriented web services