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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.

How to Do Things With Words

Statistics for Spatial Data.

The language modelling approach to information retrieval can also be used to compute query models. A query model can be envisaged as an expansion of an initial query. The more prominent query models in the literature have a probabilistic basis. This paper introduces an alternative, non-probabilistic approach to query modelling whereby the strength of information flow is computed between a query Q and a term w. Information flow is a reflection of how strongly w is informationally contained within the query Q. The information flow model is based on Hyperspace Analogue to Language (HAL) vector representations, which reflects the lexical co-occurrence information of terms. Research from cognitive science has demonstrated the cognitive compatibility of HAL representations with human processing. Query models computed from TREC queries by HAL-based information flow are compared experimentally with two probabilistic query language models. Experimental results are provided showing the HAL-based information flow model be superior to query models computed via Markov chains, and seems to be as effective as a probabilistically motivated relevance model.

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Inferring query models by computing information flow

Applying Belief Revision logic to model adaptive information retrieval is appealing since it provides a rigorous theoretical foundation to model partiality and uncertainty inherent in any information retrieval (IR) processes. In particular, a retrieval context can be formalised as a belief set and the formalised context is used to disambiguate vague user queries. Belief revision logic also provides a robust computational mechanism to revise an IR system's beliefs about the users' changing information needs. In addition, information flow is proposed as a text mining method to automatically acquire the initial IR contexts. The advantage of a belief-based IRsystem is that its IR behaviour is more predictable and explanatory. However, computational efficiency is often a concern when the belief revision formalisms are applied to large real-life applications. This paper describes our belief-based adaptive IR system which is underpinned by an efficient belief revision mechanism. Our initial experiments show that the belief-based symbolic IR model is more effective than a classical quantitative IR model. To our best knowledge, this is the first successful empirical evaluation of a logic-based IR model based on large IR benchmark collections.

/pdf/belief-revision-for-adaptive-information-retrieval-2pjy2fqw0i.pdf

Belief revision for adaptive information retrieval

Modern society demands renewed attention on the competencies required to best equip students for a dynamic and uncertain future. We present exploratory work based on the premise that metacognitive and reflective competencies are essential for this task. Bringing the concepts of metacognition and reflection together into a conceptual model within which we conceived of them as both a set of similar features, and as a spectrum ranging from the unconscious inner-self through to the conscious external social self. This model was used to guide exploratory computational analysis of 6090 instances of reflective writing authored by undergraduate students. We found the conceptual model to be useful in informing the computational analysis, which in turn showed potential for automating the discovery of metacognitive activity in reflective writing, an approach that holds promise for the generation of formative feedback for students as they work towards developing core 21st century competencies.

/pdf/towards-the-discovery-of-learner-metacognition-from-5fa0podrgz.pdf

Towards the Discovery of Learner Metacognition From Reflective Writing

This position paper envisions the emergence of a new generation of service-oriented software, namely Web Service Ecosystems, and how these systems will support large-scale collaborative business processes. The paper reviews early manifestations of these systems and identifies challenges that will need to be addressed on the road to this vision.

/pdf/the-move-to-web-service-ecosystems-1yqemkdkv8.pdf

The Move to Web Service Ecosystems

This paper is about finding explicit and implicit connections between people by mining semantic associations from their email communications. Following from a socio-cognitive stance, we propose a model called HALe which automatically derives dimensional representations of words in a high dimensional context space from an email corpus. These dimensional representations are used to discover a network of people based on a seed contextual description. Such a network represents useful connections between people not easily achievable by 'normal' retrieval means. Implicit connections are "lifted" by applying latent semantic analysis to the high dimensional context space. The discovery techniques are applied to a substantial corpus of real-life email utterance drawn from a small-to-medium size information technology organization. The techniques are computationally tractable, and evidence is presented that suggests appropriate explicit connections are being brought to light, as well as interesting, and perhaps serendipitous implicit connections. The ultimate goal of such techniques is to bring to light context-sensitive, ephemeral, and often hidden relationships between people, and between people and information, which pervade the enterprise.

Peter Bruza

Papers

Inferring query models by computing information flow

Belief revision for adaptive information retrieval

Towards the Discovery of Learner Metacognition From Reflective Writing

The Move to Web Service Ecosystems

Discovery of implicit and explicit connections between people using email utterance