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CSCW (computer-supported cooperative work) is an active research area with many promising applications and benefits. We argue that the plight of the individual user can also be viewed as a CSCW problem, for the individual frequently acts as multiple persona: performing many independent tasks, perhaps in several places. We propose reflexive CSCW to address such issues. Solutions in the reflexive case will of course be of benefit to users even if they are working in a conventional multi-user CSCW context; proposed solutions in CSCW can be re-presented for individual users.  相似文献
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Despite its simplicity, the naive Bayes learning scheme performs well on most classification tasks, and is often significantly more accurate than more sophisticated methods. Although the probability estimates that it produces can be inaccurate, it often assigns maximum probability to the correct class. This suggests that its good performance might be restricted to situations where the output is categorical. It is therefore interesting to see how it performs in domains where the predicted value is numeric, because in this case, predictions are more sensitive to inaccurate probability estimates.This paper shows how to apply the naive Bayes methodology to numeric prediction (i.e., regression) tasks by modeling the probability distribution of the target value with kernel density estimators, and compares it to linear regression, locally weighted linear regression, and a method that produces model trees—decision trees with linear regression functions at the leaves. Although we exhibit an artificial dataset for which naive Bayes is the method of choice, on real-world datasets it is almost uniformly worse than locally weighted linear regression and model trees. The comparison with linear regression depends on the error measure: for one measure naive Bayes performs similarly, while for another it is worse. We also show that standard naive Bayes applied to regression problems by discretizing the target value performs similarly badly. We then present empirical evidence that isolates naive Bayes' independence assumption as the culprit for its poor performance in the regression setting. These results indicate that the simplistic statistical assumption that naive Bayes makes is indeed more restrictive for regression than for classification.  相似文献
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Musical scores are traditionally retrieved by title, composer or subject classification. Just as multimedia computer systems increase the range of opportunities available for presenting musical information, so they also offer new ways of posing musically-oriented queries. This paper shows how scores can be retrieved from a database on the basis of a few notes sung or hummed into a microphone. The design of such a facility raises several interesting issues pertaining to music retrieval. We first describe an interface that transcribes acoustic input into standard music notation. We then analyze string matching requirements for ranked retrieval of music and present the results of an experiment which tests how accurately people sing well known melodies. The performance of several string matching criteria are analyzed using two folk song databases. Finally, we describe a prototype system which has been developed for retrieval of tunes from acoustic input and evaluate its performance.  相似文献
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Combining models learned from multiple batches of data provide an alternative to the common practice of learning one model from all the available data (i.e. the data combination approach). This paper empirically examines the base-line behavior of the model combination approach in this multiple-data-batches scenario. We find that model combination can lead to better performance even if the disjoint batches of data are drawn randomly from a larger sample, and relate the relative performance of the two approaches to the learning curve of the classifier used. In the beginning of the curve, model combination has higher bias and variance than data combination and thus a higher error rate. As training data increases, model combination has either a lower error rate than or a comparable performance to data combination because the former achieves larger variance reduction. We also show that this result is not sensitive to the methods of model combination employed. Another interesting result is that we empirically show that the near-asymptotic performance of a single model in some classification tasks can be significantly improved by combining multiple models (derived from the same algorithm) in the multiple-data-batches scenario.  相似文献
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Developing intuition for the content of a digital collection is difficult. Hierarchies of subject terms allow users to explore the space of topics that a collection covers, to form and specialize useful query terms, and to directly identify interesting documents. We describe two interfaces for navigating such hierarchies, and present a technique for inferring hierarchies automatically from large corpora. We also discuss scalability issues for the techniques involved, and our solutions to these problems. Received: 15 December 1997 / Revised: June 1999  相似文献
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Model trees, which are a type of decision tree with linear regression functions at the leaves, form the basis of a recent successful technique for predicting continuous numeric values. They can be applied to classification problems by employing a standard method of transforming a classification problem into a problem of function approximation. Surprisingly, using this simple transformation the model tree inducer M5, based on Quinlan's M5, generates more accurate classifiers than the state-of-the-art decision tree learner C5.0, particularly when most of the attributes are numeric.  相似文献
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A central problem in inductive logic programming is theory evaluation. Without some sort of preference criterion, any two theories that explain a set of examples are equally acceptable. This paper presents a scheme for evaluating alternative inductive theories based on an objective preference criterion. It strives to extract maximal redundancy from examples, transforming structure into randomness. A major strength of the method is its application to learning problems where negative examples of concepts are scarce or unavailable. A new measure called model complexity is introduced, and its use is illustrated and compared with a proof complexity measure on relational learning tasks. The complementarity of model and proof complexity parallels that of model and proof–theoretic semantics. Model complexity, where applicable, seems to be an appropriate measure for evaluating inductive logic theories.  相似文献
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It is argued that “human-centredness” will be an important characteristic of systems that learn tasks from human users, as the difficulties in inductive inference rule out learning without human assistance. The aim of “programming by example” is to create systems that learn how to perform tasks from their human users by being shown examples of what is to be done. Just as the user creates a learning environment for the system, so the system provides a teaching opportunity for the user, and emphasis is placed as much on facilitating successful teaching as on incorporating techniques of machine learning. If systems can “learn” repetitive tasks, their users will have the power to decide for themselves which parts of their jobs should be automated, and teach the system how to do them — reducing their dependence on intermediaries such as system designers and programmers. This paper presents principles for programming by example derived from experience in creating four prototype learners: for technical drawing, text editing, office tasks, and robot assembly. A teaching metaphor (a) enables the user to demonstrate a task by performing it manually, (b) helps to explain the learner's limited capabilities in terms of a persona, and (c) allows users to attribute intentionality. Tasks are represented procedurally, and augmented with constraints. Suitable mechanisms for attention focusing are necessary in order to control inductive search. Hidden features of a task should be made explicit so that the learner need not embark on the huge search entailed by hypothesizing missing steps.  相似文献
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