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learning from examples - GENERATION AND EVALUATION OF DECISION TREES FOR SOFTWARE RESOURCE ANALYSIS

IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 1988年第12期14卷 1743-1757页

作者： SELBY, RW PORTER, AA Department of Information and Computer Science University of California Irvine CA 92717

Solutions to the problem of learning from examples will have far-reaching benefits, and therefore, the problem is one of the most widely studied in the field of machine learning. The purpose of this study is to investigate a general solution method for the problem, the automatic generation Of decision (or classification) trees. The approach is to provide insights through in-depth empirical characterization and evaluation of decision trees for one problem domain, software resource data analysis. The purpose of the decision trees is to identify classes of objects (software modules) that had high development effort or faults, where 'high' was defined to be in the uppermost quartile relative to past data. Sixteen software systems ranging from 3000 to 112 000 source lines have been selected for analysis from a NASA production environment. The collection and analysis of 74 attributes (or metrics), for over 4700 objects, capture a multitude of information about the objects: development effort, faults, changes, design style, and implementation style. A total of 9600 decision trees are automatically generated and evaluated based on several parameters: 1) attribute availability; 2) evaluation function heuristic; 3) tree termination criteria; 4) number, of projects in the training set; 5) ordinal grouping of attributes; and 6) dependent variable. Sensitive 24 × 5 × 15 full-factorial analysis of variance models are employed to assess the performance contributions of the factors and their interactions simultaneously. The analysis focuses on the characterization and evaluation of decision tree accuracy, complexity, and composition. The decision trees correctly identified 79.3 percent of the software modules that had high development effort or faults, on the average across all 9600 trees. The decision trees generated from the best parameter combinations correctly identified 88.4 percent of the modules on the average. Visualization of the results is emphasized, and sample decision

关键词： Analysis of variance decision trees empirical measurement and evaluation learning from examples machine learning software effort and error analysis software metrics

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learning from examples TO AUTOMATICALLY CLUSTER PIGMENTED SKIN LESIONS

LEARNING FROM EXAMPLES TO AUTOMATICALLY CLUSTER PIGMENTED SK...

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IEEE 10th International Symposium on Biomedical Imaging - from Nano to Macro (ISBI)

作者： Wazaefi, Yanal Parisi, Sebastien Lefevrei, Julien Gaudy, Caroline Grob, Jean-Jacques Fertil, Bernard Aix Marseille Univ Lab Sci Informat & Syst Marseille France La Timone Hosp Dept Dermatol Marseille France

ISBN: (纸本)9781467364553

Our goal was to model the ability of dermatologists to build consistent clusters of pigmented skin lesions in patients. A consensus clustering allows modeling the diversity of skin lesions in each patient as a result of the partitions proposed by nine dermatologists. To learn the dermatologists' consensus clustering, we used two supervised clustering methods, namely the structural approach and the pairwise approach. These methods learnt similarity measures between individuals' skin lesions to cluster future individuals' sets of skin lesions in the same fashion as the dermatologists do. The agreement between partitions obtained from the sequential fusion of both methods and the consensus clustering matches dermatologists agreement.

关键词： Supervised clustering melanoma diagnosis consensus clustering learning from examples

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learning from examples to Generalize over Pose and Illumination

Learning from Examples to Generalize over Pose and Illuminat...

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19th International Conference on Artificial Neural Networks (ICANN 2009)

作者： Mueller, Marco K. Wuertz, Rolf P. Ruhr Univ Bochum Inst Neural Computat D-44780 Bochum Germany

ISBN: (纸本)9783642042768

We present a neural system that recognizes faces under strong variations in pose and illumination. The generalization is learnt completely on the basis of examples Of a subset of persons (the model database) in frontal and rotated view and under different Similarities in identical pose/illumination are calculated by bunch graph matching, identity is coded by similarity rank lists. A neural network based on spike timing decodes these rank lists. We show that identity decisions can be made on the basis of few spikes. Recognition results on a large database of Chinese faces show that;the transformations were successfully learnt.

关键词： rank order coding face recognition pose invariance illumination invariance learning from examples controlled generalization

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Object segmentation and labeling by learning from examples

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2003年第6期12卷 627-638页

作者： Xu, YW Saber, E Tekalp, AM Univ Rochester Dept Elect & Comp Engn Rochester NY 14627 USA Xerox Corp Webster NY 14580 USA Koc Univ Coll Engn Istanbul Turkey

We propose a system that employs low-level image segmentation followed by color and two-dimensional (2-D) shape matching to automatically group those low-level segments into objects based on their similarity to a set of example object templates presented by the user. A hierarchical content tree data structure is used for each database image to store matching combinations of low-level regions as objects. The system automatically initializes the content tree with only "elementary nodes" representing homogeneous low-level regions. The "learning" phase refers to labeling of combinations of low-level regions that have resulted in successful color and/or 2-D shape matches with the example template(s). These combinations are labeled as "object nodes" in the hierarchical content tree. Once learning is performed, the speed of second-time retrieval of learned objects in the database increases significantly. The learning step can be performed off-line provided that example objects are given in the form of user interest profiles. Experimental results are presented to demonstrate the effectiveness of the proposed system with hierarchical content tree representation and learning by color and 2-D shape matching on collections of car and face images.

关键词： color matching learning from examples object annotation semantic object segmentation shape matching

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POPULATION-BASED learning - A METHOD FOR learning from examples UNDER RESOURCE CONSTRAINTS

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 1992年第5期4卷 454-474页

作者： WAH, BW UNIV ILLINOIS DEPT ELECT & COMP ENGN URBANA IL 61801 USA

In this paper, we study a learning model for designing heuristics automatically under resource constraints. We focus on improving performance related heuristic methods (HM's) in knowledge lean application domains. We assume that learning is episodic, that the performance measures of an episode are dependent only on the final state reached in evaluating the corresponding test case and are independent of the intermediate decisions and internal states, and that the aggregate performance measures of the HM's involved are independent of the order of evaluation of test cases. The learning model is based on testing a population of competing HM's for an application problem, and switches from one to another dynamically, depending on the outcome of previous tests. Its goal is to find a good HM within the resource constraints, with proper trade-off between cost and quality. It complements existing point-based machine learning models that maintain only one incumbent HM, and that test the HM extensively before switching to alternative ones. It extends existing work on classifier systems by addressing issues related to delays in feedback, scheduling of tests of HM's under limited resources, anomalies in performance evaluation, and scalability of HM's. Finally, we describe our experience in applying the learning method on five application problems.

关键词： HEURISTIC METHOD KNOWLEDGE-LEAN APPLICATION learning from examples PERFORMANCE EVALUATION POINT-BASED learning POPULATION-BASED learning RESOURCE SCHEDULING

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On the role of search for learning from examples

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JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE 2001年第1期13卷 25-43页

作者： Kurtz, SA Smith, CH Wiehagen, R Univ Chicago Dept Comp Sci Chicago IL 60637 USA Univ Maryland Dept Comp Sci College Pk MD 20742 USA Univ Kaiserslautern Dept Comp Sci D-67653 Kaiserslautern Germany

Gold (1967) discovered a fundamental enumeration technique, the so-called identification-by-enumeration, a simple but powerful class of algorithms for learning from examples (inductive inference). We introduce a variety of more sophisticated (and more powerful) enumeration techniques and characterize their power. We conclude with the thesis that enumeration techniques are even universal in that each solvable learning problem in inductive inference can be solved by an adequate enumeration technique. This thesis is technically motivated and discussed.

关键词： learning from examples learning by search identification by enumeration enumeration techniques

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AN INCREMENTAL DEDUCTIVE STRATEGY FOR CONTROLLING CONSTRUCTIVE INDUCTION IN learning from examples

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MACHINE learning 1991年第1期7卷 7-44页

作者： ELIO, R WATANABE, L 1. Department of Computing Science The University of Alberta Edmonton Alberta Canada T6G 2H1. 2. Department of Computing Science The University of Alberta Edmonton Alberta Canada T6G 2H1

This article describes LAIR, a constructive induction system that acquires conjunctive concepts by applying a domain theory to introduce new features into the evolving concept description. Each acquired concept is added to the domain theory, making LAIR a closed-loop learning system that weakens the inductive bias with each iteration of the learning loop. LAIR's novel feature is the use of an incremental deductive strategy for constructive induction, reducing the amount of inference required for learning. A series of experiments manipulated features of learning tasks to assess this incremental method of constructive induction relative to an uncontrolled constructive induction process that extends each example description with all derivable features. These learning tasks differed in global characteristics of the domain theory, the training sequence, and the percentage of irrelevant features in the example descriptions. The results show that LAIR's constructive induction approach saves considerable inferencing effort, with little or no cost in the number of examples needed to reach a learning criterion. The experimental results also underscored the importance of viewing a domain theory as a search space, identifying several factors that impact the deductive and inductive aspects of constructive induction, such as concept definition overlap, density of features, and fan-in and fan-out of inference chains. The paper also discusses LAIR's operation as a pac-learner and its relation to other constructive induction techniques.

关键词： CONSTRUCTIVE INDUCTION learning from examples KNOWLEDGE ACQUISITION DOMAIN KNOWLEDGE AND learning

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learning from examples does not prevent order effects in belief revision

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Thinking & Reasoning 2010年第2期16卷 98-130页

A common finding is that information order influences belief revision (e.g., Hogarth & Einhorn,1992Hogarth,R. ***,H. *** effects in belief updating: The belief-adjustment *** Psychology, 24:1–55.[Crossref],[Web of Science ®],[Google Scholar]). We tested personal experience as a possible mitigator. In three experiments participants experienced the probabilistic relationship between pieces of information and object category through a series of trials where they assigned objects (planes) into one of two possible categories (hostile or commercial), given two sequentially presented pieces of probabilistic information (route and ID), and then they had to indicate their belief about the object category before feedback. The results generally confirm the predictions from the Hogarth and Einhorn model. Participants showed a recency effect in their belief revision. Extending previous model evaluations the results indicate that the model predictions also hold for classification decisions, and for pieces of information that vary in their diagnostic values. Personal experience does not appear to prevent order effects in classification decisions based on sequentially presented pieces of information and in belief revision.

关键词： Belief revision Opinion revision Order effects learning from examples Probability learning

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The algorithm AE_(11) of learning from examples

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Journal of Harbin Institute of Technology(New Series) 2006年第2期13卷 226-232页

作者： ZHANG Hai-yi BI Jian-dong Jodrey School of Computer Science Acadia University Wolfville Nova Scotia Canada B4P 2R6 Dept. of Computer Science Harbin Institute of Technology Harbin 150001 China

We first put forward the idea of a positive extension matrix (PEM) on paper. Then, an algorithm, AE_ 11, was built with the aid of the PEM. Finally, we made the comparisons of our experimental results and the final re... 详细信息

关键词： learning from examples concept acquisition inductive learning knowledge acquisition

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learning from cluster examples

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MACHINE learning 2003年第3期53卷 199-233页

作者： Kamishima, T Motoyoshi, F Natl Inst Adv Ind Sci & Technol AIST Tsukiuba Cent 2 Tsukuba Ibaraki 3058568 Japan

learning from cluster examples (LCE) is a hybrid task combining features of two common grouping tasks: learning from examples and clustering. In LCE, each training example is a partition of objects. The task is then to learn from a training set, a rule for partitioning unseen object sets. A general method for learning such partitioning rules is useful in any situation where explicit algorithms for deriving partitions are hard to formalize, while individual examples of correct partitions are easy to specify. In the past, clustering techniques have been applied to such problems, despite being essentially unsuited to the task. We present a technique that has qualitative advantages over standard clustering approaches. We demonstrate these advantages by applying our method to problems in two domains;one with dot patterns and one with more realistic vector-data images.

关键词： learning from examples clustering dot pattern image segmentation

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