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19th International Conference on Computers in Education, ICCE 2011

作者： Kojima, Kazuaki Miwa, Kazuhisa Matsui, Tatsunori Faculty of Human Sciences Waseda University Japan Graduate School of Information Science Nagoya University Japan

ISBN: (纸本)9786161201883

Problem posing is identified as an important activity in mathematics education. Although it is difficult for novice learners to pose diverse problems, they must do so. Such novices particularly have difficulty in appropriately composing mathematical structures of solutions when posing new problems. This paper discussed and experimentally studied an approach to support learning of problem posing as a production task. We designed support for learning by imitation of examples, in which learners reproduce problems presented as examples. That is expected to facilitate learners' understanding ideas used in generation of the examples, and to stimulate new idea generation in learners' own problem posing. We then verified the effects of learning an example through imitation, with the results indicating that learning by imitation can facilitate adaptation of ideas used in an example into learners' problem posing, whereas it cannot necessarily facilitate appropriate problem posing when they try to diversify.

关键词： Idea generation learning from examples Mathematical learning Problem posing Solution strictures

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Wireless Device Connection Problems and Design Solutions

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Chinese Journal of Mechanical Engineering 2016年第6期29卷 1145-1155页

作者： SONG Ji-Won NORMAN Donald NAM Tek-Jin QIN Shengfeng College of Engineering Design and Physical Sciences Brunel University London The Design Lab University of California Department of Industrial Design Korea Advanced Institute of Science and Technology Department of Design Northumbria University

Users, especially the non-expert users, commonly experience problems when connecting multiple devices with interoperability. While studies on multiple device connections are mostly concentrated on spontaneous device association techniques with a focus on security aspects, the research on user interaction for device connection is still limited. More research into understanding people is needed for designers to devise usable techniques. This research applies the Research-through-Design method and studies the non-expert users＇ interactions in establishing wireless connections between devices. The ＂learning from examples＂ concept is adopted to develop a study focus line by learning from the expert users＇ interaction with devices. This focus line is then used for guiding researchers to explore the non-expert users＇ difficulties at each stage of the focus line. Finally, the Research-through-Design approach is used to understand the users＇ difficulties, gain insights to design problems and suggest usable solutions. When connecting a device, the user is required to manage not only the device＇s functionality but also the interaction between devices. Based on learning from failures, an important insight is found that the existing design approach to improve single-device interaction issues, such as improvements to graphical user interfaces or computer guidance, cannot help users to handle problems between multiple devices. This study finally proposes a desirable user-device interaction in which images of two devices function together with a system image to provide the user with feedback on the status of the connection, which allows them to infer any required actions.

关键词： wireless connection device association user-multiple device interaction smart production system Research through Design learning from examples

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learning to recognize volcanoes on Venus

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MACHINE learning 1998年第2-3期30卷 165-194页

作者： Burl, MC Asker, L Smyth, P Fayyad, U Perona, P Crumpler, L Aubele, J CALTECH Jet Prop Lab Pasadena CA 91109 USA Univ Stockholm S-10691 Stockholm Sweden Univ Calif Irvine Irvine CA 92717 USA Univ Padua I-35100 Padua Italy Brown Univ Providence RI 02912 USA New Mexico Museum Nat Hist & Sci Albuquerque NM USA Microsoft Res Redmond WA USA

Dramatic improvements in sensor and image acquisition technology have created a demand for automated tools that can aid in the analysis of large image databases. We describe the development of JARtool, a trainable software system that learns to recognize volcanoes in a large data set of Venusian imagery. A machine learning approach is used because it is much easier for geologists to identify examples of volcanoes in the imagery than it is to specify domain knowledge as a set of pixel-level constraints. This approach can also provide portability to other domains without the need for explicit reprogramming;the user simply supplies the system with a new set of training examples. We show how the development of such a system requires a completely different set of skills than are required for applying machine learning to "toy world" domains. This paper discusses important aspects of the application process not commonly encountered in the "toy world" including obtaining labeled training data, the difficulties of working with pixel data, and the automatic extraction of higher-level features.

关键词： machine learning pattern recognition learning from examples large image databases data mining automatic cataloging detection of natural objects Magellan SAR JARtool volcanoes Venus principal components analysis trainable

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THE STRENGTH OF WEAK LEARNABILITY

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MACHINE learning 1990年第2期5卷 197-227页

作者： SCHAPIRE, RE 1. MIT Laboratory for Computer Science 545 Technology Square 02139 Cambridge MA

This paper addresses the problem of improving the accuracy of an hypothesis output by a learning algorithm in the distribution-free (PAC) learning model. A concept class islearnable (orstrongly learnable) if, given access to a source of examples of the unknown concept, the learner with high probability is able to output an hypothesis that is correct on all but an arbitrarily small fraction of the instances. The concept class isweakly learnable if the learner can produce an hypothesis that performs only slightly better than random guessing. In this paper, it is shown that these two notions of learnability are equivalent. A method is described for converting a weak learning algorithm into one that achieves arbitrarily high accuracy. This construction may have practical applications as a tool for efficiently converting a mediocre learning algorithm into one that performs extremely well. In addition, the construction has some interesting theoretical consequences, including a set of general upper bounds on the complexity of any strong learning algorithm as a function of the allowed error ο.

关键词： Machine learning learning from examples learnability theory PAC learning polynomial-time identification

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ROBUSTIZATION OF A learning-METHOD FOR RBF NETWORKS

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NEUROCOMPUTING 1995年第1期9卷 85-94页

作者： SANCHEZ, VD German Aerospace Research Establishment DLR Oberpfaffenhofen Institute for Robotics and System Dynamics D-82230 Wessling Germany

Outliers in a given training data set can cause substantial deterioration of the approximation realized by a neural network architecture. Robustization refers to the process of enhancing a learning method to deal with data containing outliers. The robustization of a learning method for training RBF networks for function approximation is presented leading to a novel efficient robust learning method for this architecture class. Its experimental evaluation confirms the expectations created from its theoretical foundations.

关键词： FUNCTION APPROXIMATION learning from examples NETWORK SYNTHESIS RADIAL BASIS FUNCTIONS REGRESSION ROBUST learning METHODS

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RECURSIVE AUTOMATIC BIAS SELECTION FOR CLASSIFIER CONSTRUCTION

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MACHINE learning 1995年第1-2期20卷 63-94页

作者： BRODLEY, CE School of Electrical and Computer Engineering Purdue University West Lafayette IN 47907.brodley@ecn.purdue.edu

The results of empirical comparisons DE existing learning algorithms illustrate that each algorithm has a selective superiority;each is best for some but not ail tasks. Given a data set, it is often not clear beforehand which algorithm will yield the best performance. In this article we present an approach that uses characteristics of the given data set, in the form of feedback from the learning process, to guide a search for a tree-structured hybrid classifier. Heuristic knowledge about the characteristics that indicate one bias is better than another is encoded in the rule base of the Model Class Selection (MCS) system. The approach does not assume that the entire instance space is best learned using a single representation language;for some data sets, choosing to form a hybrid classifier is a better bias, and MCS has the ability to determine these cases. The results of an empirical evaluation illustrate that MCS achieves classification accuracies equal to or higher than the best of its primitive learning components for each data set, demonstrating that the heuristic rules effectively select an appropriate learning bias.

关键词： INDUCTIVE BIAS HYBRID CLASSIFIERS AUTOMATIC ALGORITHM SELECTION DECISION TREES learning from examples

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An approach to guided learning of Boolean functions

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MATHEMATICAL AND COMPUTER MODELLING 1996年第3期23卷 69-86页

作者： Triantaphyllou, E Soyster, AL PENN STATE UNIV DEPT IND & MANAGEMENT SYST ENGN UNIVERSITY PK PA 16802 USA

A critical aspect in the problem of inductive inference is the number of examples needed to accurately infer a Boolean function from positive and negative examples. In this paper, we develop an approach for deriving a sequence of examples for this problem. Some computer experiments indicate that, on the average, examples derived according to the proposed approach lead to the inference of the correct function considerably faster than when examples are derived in a random order.

关键词： inductive inference Boolean functions learning from examples artificial intelligence clause satisfiability problem

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Machine learning in prognosis of the femoral neck fracture recovery

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ARTIFICIAL INTELLIGENCE IN MEDICINE 1996年第5期8卷 431-451页

作者： Kukar, M Kononenko, I Silvester, T UNIV LJUBLJANA FAC COMP & INFORMAT SCI LJUBLJANA 61001 SLOVENIA UNIV LJUBLJANA FAC MED LJUBLJANA 61001 SLOVENIA

We compare the performance of several machine learning algorithms in the problem of prognostics of the femoral neck fracture recovery: the K-nearest neighbours algorithm, the semi-naive Bayesian classifier, backpropagation with weight elimination learning of the multilayered neural networks, the LFC (lookahead feature construction) algorithm, and the Assistant-I and Assistant-R algorithms for top down induction of decision trees using information gain and RELIEFF as search heuristics, respectively. We compare the prognostic accuracy and the explanation ability of different classifiers. Among the different algorithms the semi-naive Bayesian classifier and Assistant-R seem to be the most appropriate. We analyze the combination of decisions of several classifiers for solving prediction problems and show that the combined classifier improves both performance and the explanation ability.

关键词： learning from examples estimating attributes explanation ability impurity function empirical comparison multiple knowledge

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BEXA: A covering algorithm for learning propositional concept descriptions

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MACHINE learning 1996年第1期24卷 5-40页

作者： Theron, H Cloete, I Department of Computer Science University of Stellenbosch Stellenbosch South Africa

BEXA is a new covering algorithm for inducing propositional concept descriptions. Existing covering algorithms such as AQ15 and CN2 place rigid constraints on the search process to reduce the learning time. These restrictions may allow useless specializations while at the same time ignoring potentially useful specializations. in contrast BEXA employs three dynamic search constraints that enable it to find simple and accurate concept descriptions efficiently. This paper describes the BEXA algorithm and its relationship to the covering algorithms AQ15, CN2, GREEDY3, PRISM, and an algorithm proposed by Gray. The specialization models of these algorithms are described in the uniform framework of specialization by exclusion of values. BEXA is compared empirically to state-of-the-art concept learners CN2 and C4.5. It produces rules of comparable accuracy, but with greater simplicity.

关键词： learning from examples covering algorithms irredundant set covers internal disjunction

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Satisfiability decay along conjunctions of pseudo-random clauses

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LOGIC JOURNAL OF THE IGPL 2006年第5期14卷 815-825页

作者： Shamir, Eli Hebrew Univ Jerusalem Inst Math IL-91905 Jerusalem Israel Hebrew Univ Jerusalem Sch Comp Sci IL-91905 Jerusalem Israel

k-SAT is a fundamental constraint satisfaction problem. It involves S(m), the satisfaction set of the conjunction of m clauses, each clause a disjunction of k literals. The problem has many theoretical, algorithmic and practical aspects. When the clauses are chosen at random it is anticipated (but not fully proven) that, as the density parameter m/n (n the number of variables) grows, the transition of S(m) to being empty, is abrupt: It has a "sharp threshold", with probability 1 - o(1). In this article we replace the random ensemble analysis by a pseudo-random one: Derive the decay rule for individual sequences of clauses, subject to combinatorial conditions, which in turn hold with probability 1 - o(1). This is carried out under the big relaxation that k is not constant but k = gamma log n, or even r log log n. Then the decay of S is slow, "near-perfect" (like a radioactive decay), which entails sharp thresholds for the transition-time of S below any given level, down to S = 0.

关键词： constraint satisfaction k-SAT sharp thresholds pseudorandom analysis unique k-SAT representations learning from examples

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