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Journal articles on the topic 'Inductive learning'

Author: Grafiati
Published: 4 June 2021
Last updated: 31 January 2023

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1

Xindong, Wu. "Inductive learning." Journal of Computer Science and Technology 8, no. 2 (April 1993): 118–32. http://dx.doi.org/10.1007/bf02939474.

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2

Chan, T. Y. T. "Inductive pattern learning." IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 29, no. 6 (1999): 667–74. http://dx.doi.org/10.1109/3468.798072.

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3

Hadjimichael, Michael, and Anita Wasilewska. "Interactive inductive learning." International Journal of Man-Machine Studies 38, no. 2 (February 1993): 147–67. http://dx.doi.org/10.1006/imms.1993.1008.

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4

Kubat, Miroslav. "Conceptual inductive learning." Artificial Intelligence 52, no. 2 (December 1991): 169–82. http://dx.doi.org/10.1016/0004-3702(91)90041-h.

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5

Pham, D. T., S. Bigot, and S. S. Dimov. "RULES-F: A fuzzy inductive learning algorithm." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 220, no. 9 (September 1, 2006): 1433–47. http://dx.doi.org/10.1243/0954406c20004.

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Current inductive learning algorithms have difficulties handling attributes with numerical values. This paper presents RULES-F, a new fuzzy inductive learning algorithm in the RULES family, which integrates the capabilities and performance of a good inductive learning algorithm for classification applications with the ability to create accurate and compact fuzzy models for the generation of numerical outputs. The performance of RULES-F in two simulated control applications involving numerical output parameters is demonstrated and compared with that of the well-known fuzzy rule induction algorithm by Wang and Mendel.
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6

Santos, Paulo, Chris Needham, and Derek Magee. "Inductive learning spatial attention." Sba: Controle & Automação Sociedade Brasileira de Automatica 19, no. 3 (September 2008): 316–26. http://dx.doi.org/10.1590/s0103-17592008000300007.

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This paper investigates the automatic induction of spatial attention from the visual observation of objects manipulated on a table top. In this work, space is represented in terms of a novel observer-object relative reference system, named Local Cardinal System, defined upon the local neighbourhood of objects on the table. We present results of applying the proposed methodology on five distinct scenarios involving the construction of spatial patterns of coloured blocks.
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7

Liu, Xiaobo. "Ensemble Inductive Transfer Learning." Journal of Fiber Bioengineering and Informatics 8, no. 1 (June 2015): 105–15. http://dx.doi.org/10.3993/jfbi03201510.

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8

Russell, Stuart. "Inductive learning by machines." Philosophical Studies 64, no. 1 (October 1991): 37–64. http://dx.doi.org/10.1007/bf00356089.

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9

Ray, Oliver. "Nonmonotonic abductive inductive learning." Journal of Applied Logic 7, no. 3 (September 2009): 329–40. http://dx.doi.org/10.1016/j.jal.2008.10.007.

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10

Rahmatian, Rouhollah, and Fatemeh Zarekar. "Inductive/Deductive Learning by Considering the Role of Gender—A Case Study of Iranian French-Learners." International Education Studies 9, no. 12 (November 28, 2016): 254. http://dx.doi.org/10.5539/ies.v9n12p254.

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<p class="apa">This article defines the objective of discovering the first preferred styles of Iranian learners of French as a Foreign Language (FFL) as regards inductive or deductive learning; and secondly, the difference between gender-based learning tendencies. Considering these points as target variables, the questionnaire developed by Felder and Silverman in 1988 was applied to form the learning styles and consequently to associate them with inductive and deductive approaches. The results led the team to set the idea which is based on the choice of induction or deduction in language learning and the gender variable that follows different directions. Consequently, in terms of the inductive approach, we find ourselves facing a rather male solicitation. A proportion of the use of this approach is also associated with women whose motivation is seen rather noticeably. Moreover, the significance is relative rather than significant in all the relationships studied in this research: males and inductive (1)/deductive learning (2); females and inductive (3)/deductive learning (4); inductive (5)/deductive (6) among Iranians.</p>
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11

MARKOV, ZDRAVKO. "AN ALGEBRAIC APPROACH TO INDUCTIVE LEARNING." International Journal on Artificial Intelligence Tools 10, no. 01n02 (March 2001): 257–72. http://dx.doi.org/10.1142/s0218213001000519.

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The paper presents a framework to induction of concept hierarchies based on consistent integration of metric and similarity-based approaches. The hierarchies used are subsumption lattices induced by the least general generalization operator (lgg) commonly used in inductive learning. Using some basic results from lattice theory the paper introduces a semantic distance measure between objects in concept hierarchies and discusses its applications for solving concept learning and conceptual clustering tasks. Experiments with well known ML datasets represented in three types of languages - propositional (attribute-value), atomic formulae and Horn clauses, are also presented.
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12

Dzeroski, S., and N. Lavrac. "Inductive learning in deductive databases." IEEE Transactions on Knowledge and Data Engineering 5, no. 6 (1993): 939–49. http://dx.doi.org/10.1109/69.250076.

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13

Cios, Krzysztof J., and Ian Moraes. "ALFS: An Inductive Learning Algorithm." Kybernetes 20, no. 3 (March 1991): 18–29. http://dx.doi.org/10.1108/eb005885.

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14

Rossi, Ryan A., Rong Zhou, and Nesreen K. Ahmed. "Deep Inductive Graph Representation Learning." IEEE Transactions on Knowledge and Data Engineering 32, no. 3 (March 1, 2020): 438–52. http://dx.doi.org/10.1109/tkde.2018.2878247.

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15

Williams, John N. "Memory, Attention, and Inductive Learning." Studies in Second Language Acquisition 21, no. 1 (March 1999): 1–48. http://dx.doi.org/10.1017/s0272263199001011.

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Three experiments investigated the relationship between memory for input and inductive learning of morphological rules relating to functional categories in a semiartificial form of Italian. A verbatim memory task was used as both the vehicle for presenting sentences and as a continuous measure of memory performance. Experiments 2 and 3 introduced increasingly explicit manipulations of attention to form compared to Experiment 1. In all experiments there were strong relationships between individual differences in memory for input as measured early in the experiment and eventual learning outcomes, and in Experiments 2 and 3 learning form-form (but not form-function) rules was related to vocabulary learning efficiency (taken as a measure of phonological long-term memory ability). These relationships along with the lack of an effect of feedback in Experiment 3 suggest that subjects tended to adopt a data-driven, as opposed to conceptually driven, mode of learning. However, the fact that the introduction of highlighting and vocabulary pretraining in Experiment 2 had a large impact on learning without improving early memory is taken to suggest that knowledge of distributional rules does not simply emerge out of memory encodings of the relevant forms but depends upon the appropriate allocation of attention over relationships between input elements at the time of encoding.
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16

Shaw, M. J., and J. A. Gentry. "Inductive learning for risk classification." IEEE Expert 5, no. 1 (February 1990): 47–53. http://dx.doi.org/10.1109/64.50856.

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17

CORAPI, DOMENICO, ALESSANDRA RUSSO, MARINA DE VOS, JULIAN PADGET, and KEN SATOH. "Normative design using inductive learning." Theory and Practice of Logic Programming 11, no. 4-5 (July 2011): 783–99. http://dx.doi.org/10.1017/s1471068411000305.

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AbstractIn this paper we propose a use-case-driven iterative design methodology for normative frameworks, also called virtual institutions, which are used to govern open systems. Our computational model represents the normative framework as a logic program under answer set semantics (ASP). By means of an inductive logic programming approach, implemented using ASP, it is possible to synthesise new rules and revise the existing ones. The learning mechanism is guided by the designer who describes the desired properties of the framework through use cases, comprising (i) event traces that capture possible scenarios, and (ii) a state that describes the desired outcome. The learning process then proposes additional rules, or changes to current rules, to satisfy the constraints expressed in the use cases. Thus, the contribution of this paper is a process for the elaboration and revision of a normative framework by means of a semi-automatic and iterative process driven from specifications of (un)desirable behaviour. The process integrates a novel and general methodology for theory revision based on ASP.
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18

Gemello, Roberto, Franco Mana, and Lorenza Saitta. "Rigel: An inductive learning system." Machine Learning 6, no. 1 (January 1991): 7–35. http://dx.doi.org/10.1007/bf00153758.

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19

Lukac, Martin, and Marek Perkowski. "Inductive learning of quantum behaviors." Facta universitatis - series: Electronics and Energetics 20, no. 3 (2007): 561–86. http://dx.doi.org/10.2298/fuee0703561l.

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In this paper studied are new concepts of robotic behaviors - deterministic and quantum probabilistic. In contrast to classical circuits, the quantum circuit can realize both of these behaviors. When applied to a robot, a quantum circuit controller realizes what we call quantum robot behaviors. We use automated methods to synthesize quantum behaviors (circuits) from the examples (examples are cares of the quantum truth table). The don't knows (minterms not given as examples) are then converted not only to deterministic cares as in the classical learning, but also to output values generated with various probabilities. The Occam Razor principle, fundamental to inductive learning, is satisfied in this approach by seeking circuits of reduced complexity. This is illustrated by the synthesis of single output quantum circuits, as we extended the logic synthesis approach to Inductive Machine Learning for the case of learning quantum circuits from behavioral examples.
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20

KORB, KEVIN B. "Inductive learning and defeasible inference." Journal of Experimental & Theoretical Artificial Intelligence 7, no. 3 (July 1995): 291–324. http://dx.doi.org/10.1080/09528139508953814.

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21

Abbott, Lynn. "Cohesion methods in inductive learning." Computational Intelligence 3, no. 1 (February 1987): 267–82. http://dx.doi.org/10.1111/j.1467-8640.1987.tb00214.x.

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22

CERANS, KARLIS H., and CARL H. SMITH. "Self-learning inductive inference machines." Computational Intelligence 7, no. 3 (August 1991): 174–80. http://dx.doi.org/10.1111/j.1467-8640.1991.tb00392.x.

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23

Gollin, Jacqueline. "Deductive vs. inductive language learning." ELT Journal 52, no. 1 (January 1998): 88–89. http://dx.doi.org/10.1093/elt/52.1.88.

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24

Szczerbicka, Rainer Barton, Helena. "INDUCTIVE LEARNING FOR PARAMETER OPTIMIZATION." Cybernetics and Systems 31, no. 5 (July 2000): 469–90. http://dx.doi.org/10.1080/01969720050045985.

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25

Amit, Ron, and Ron Meir. "Lifelong learning and inductive bias." Current Opinion in Behavioral Sciences 29 (October 2019): 51–54. http://dx.doi.org/10.1016/j.cobeha.2019.04.003.

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26

Wu, Xindong. "Inductive learning: Algorithms and frontiers." Artificial Intelligence Review 7, no. 2 (April 1993): 93–108. http://dx.doi.org/10.1007/bf00849079.

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27

Pham, D. T., and S. S. Dimov. "An algorithm for incremental inductive learning." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 211, no. 3 (March 1, 1997): 239–49. http://dx.doi.org/10.1243/0954405971516239.

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This paper describes RULES-4, a new algorithm for incremental inductive learning from the ‘RULES’ family of automatic rule extraction systems. This algorithm is the first incremental learning system in the family. It has a number of advantages over well-known non-incremental schemes. It allows the stored knowledge to be updated and refined rapidly when new examples are available. The induction of rules for a process planning expert system is used to illustrate the operation of RULES-4 and a bench-mark pattern classification problem employed to test the algorithm. The results obtained have shown that the accuracy of the extracted rule sets is commensurate with the accuracy of the rule set obtained using a non-incremental algorithm.
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28

Sakurai, Shigeaki. "Learning of prediction rule using fuzzy inductive learning." IEEJ Transactions on Electronics, Information and Systems 118, no. 9 (1998): 1369–75. http://dx.doi.org/10.1541/ieejeiss1987.118.9_1369.

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29

Schurz, Gerhard. "META-INDUCTION IN EPISTEMIC NETWORKS AND THE SOCIAL SPREAD OF KNOWLEDGE." Episteme 9, no. 2 (June 2012): 151–70. http://dx.doi.org/10.1017/epi.2012.6.

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AbstractIndicators of the reliability of informants are essential for social learning in a society that is initially dominated by ignorance or superstition. Such reliability indicators should be based on meta-induction over records of truth-success. This is the major claim of this paper, and it is supported in two steps. (1) One needs a non-circular justification of the method of meta-induction, as compared to other (non-inductive) learning methods. An approach to this problem (a variant of Hume's problem) has been developed in earlier papers and is reported in section 2. It is based on the predictive optimality of meta-inductive learning, under the assumption that objective success records are globally available. (2) The rest of the paper develops an extension of this approach, so-called local meta-induction. Here individuals can access only success records of individuals in their immediate epistemic neighborhood. It is shown that local meta-inductive learning can spread reliable information over the entire population, and has clear advantages compared to success-independent social learning methods such as peer-imitation and authority-imitation.
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30

ElGibreen, Hebah, and Mehmet Sabih Aksoy. "Inductive Learning for Continuous Classes and the Effect of RULES Family." International Journal of Information and Education Technology 5, no. 8 (2015): 564–70. http://dx.doi.org/10.7763/ijiet.2015.v5.569.

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31

Mihai, Dana, and Mihai Mocanu. "Processing GIS Data Using Decision Trees and an Inductive Learning Method." International Journal of Machine Learning and Computing 11, no. 6 (November 2021): 393–98. http://dx.doi.org/10.18178/ijmlc.2021.11.6.1067.

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32

Rosyad, Ali Miftakhu. "THE IMPLEMENTATION OF INDUCTIVE TEACHING AND LEARNING METHODS IN ISLAMIC EDUCATION LEARNING." Risâlah, Jurnal Pendidikan dan Studi Islam 6, no. 1 (September 5, 2019): 60–75. http://dx.doi.org/10.31943/jurnal_risalah.v6i1.107.

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The article aims to describe the essence of inductive method in Islamic education learning. The learning will run optimally if the teacher utilize the suitable approach and method.Traditional engineering instruction is deductive, beginning with theories and progressing to applications of those theories. Alternative teaching approaches are more inductive. The Islamic education learning should be utilized the inductive method. Topics are introduced by presenting specific observations, case studies or problems, and theories are taught or the students are helped to discover them only after the need to know them has been established. Factually, in modern era the Islamic education learning must be innovated for answering the globalization demand.
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33

Mizoguchi, Fumio. "Learning in robotics. Inductive Learning Approach to Qualitative Physics." Journal of the Robotics Society of Japan 13, no. 1 (1995): 32–37. http://dx.doi.org/10.7210/jrsj.13.32.

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34

Zhang, Chuxu, Huaxiu Yao, Lu Yu, Chao Huang, Dongjin Song, Haifeng Chen, Meng Jiang, and Nitesh V. Chawla. "Inductive Contextual Relation Learning for Personalization." ACM Transactions on Information Systems 39, no. 3 (May 22, 2021): 1–22. http://dx.doi.org/10.1145/3450353.

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Web personalization, e.g., recommendation or relevance search, tailoring a service/product to accommodate specific online users, is becoming increasingly important. Inductive personalization aims to infer the relations between existing entities and unseen new ones, e.g., searching relevant authors for new papers or recommending new items to users. This problem, however, is challenging since most of recent studies focus on transductive problem for existing entities. In addition, despite some inductive learning approaches have been introduced recently, their performance is sub-optimal due to relatively simple and inflexible architectures for aggregating entity’s content. To this end, we propose the inductive contextual personalization (ICP) framework through contextual relation learning. Specifically, we first formulate the pairwise relations between entities with a ranking optimization scheme that employs neural aggregator to fuse entity’s heterogeneous contents. Next, we introduce a node embedding term to capture entity’s contextual relations, as a smoothness constraint over the prior ranking objective. Finally, the gradient descent procedure with adaptive negative sampling is employed to learn the model parameters. The learned model is capable of inferring the relations between existing entities and inductive ones. Thorough experiments demonstrate that ICP outperforms numerous baseline methods for two different applications, i.e., relevant author search and new item recommendation.
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35

M.AlMana, Amal, and Mohmet Aksoy. "An Overview of Inductive Learning Algorithms." International Journal of Computer Applications 88, no. 4 (February 14, 2014): 20–28. http://dx.doi.org/10.5120/15340-3675.

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36

Baxter, J. "A Model of Inductive Bias Learning." Journal of Artificial Intelligence Research 12 (March 1, 2000): 149–98. http://dx.doi.org/10.1613/jair.731.

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A major problem in machine learning is that of inductive bias: how to choose a learner's hypothesis space so that it is large enough to contain a solution to the problem being learnt, yet small enough to ensure reliable generalization from reasonably-sized training sets. Typically such bias is supplied by hand through the skill and insights of experts. In this paper a model for automatically learning bias is investigated. The central assumption of the model is that the learner is embedded within an environment of related learning tasks. Within such an environment the learner can sample from multiple tasks, and hence it can search for a hypothesis space that contains good solutions to many of the problems in the environment. Under certain restrictions on the set of all hypothesis spaces available to the learner, we show that a hypothesis space that performs well on a sufficiently large number of training tasks will also perform well when learning novel tasks in the same environment. Explicit bounds are also derived demonstrating that learning multiple tasks within an environment of related tasks can potentially give much better generalization than learning a single task.
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37

SUWA, Haruhiko, Hiroshi MORITA, and Susumu FUJII. "Scheduling Rules Acquisition by Inductive Learning." Transactions of the Institute of Systems, Control and Information Engineers 10, no. 9 (1997): 463–69. http://dx.doi.org/10.5687/iscie.10.463.

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38

Murayama, Takeshi, Bungo Takemura, and Fuminori Oba. "Assembly Sequence Planning Using Inductive Learning." Journal of Robotics and Mechatronics 11, no. 4 (August 20, 1999): 315–20. http://dx.doi.org/10.20965/jrm.1999.p0315.

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The authors propose acquiring heuristic rules automatically for generating assembly sequences efficiently. Heuristic rules are reduced from training examples by inductive learning. Additional training examples are made from information on assembly sequences and used for modifying heuristic rules. As the assembly sequence generation and modification of heuristic rules are executed more, heuristic rules are refined and assembly sequences are generated efficiently. An experiment demonstrated the effectiveness of the approach.
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39

Nakayama, Shoichiro, and Ryuichi Kitamura. "Route Choice Model with Inductive Learning." Transportation Research Record: Journal of the Transportation Research Board 1725, no. 1 (January 2000): 63–70. http://dx.doi.org/10.3141/1725-09.

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In this study drivers are assumed to reason and learn inductively based on the theory of cognitive psychology. The model system is basically a production system, a compilation of if-then rules in which the rules are revised by applying genetic algorithms. The behavior of drivers and network flow through Monte Carlo simulation using the model system is examined. The intention of this research is to shed light on the behavior of a driver-network system from a new standpoint, one different from that of equilibrium analysis. This research views drivers’ behaviors as psychological and heterogeneous rather than economical and homogeneous. The results of the numerical experiments can be summarized as follows: (1) network flow does not necessarily converge to the user equilibrium; (2) drivers form a delusion, an extremely biased perception of travel time as a result of experiencing excessive travel times on early parts of the simulation in which little experience had been gained; (3) the delusion is dissolved by switching routes capriciously; and (4) without caprice drivers continue to travel on the same route because of their delusions and develop the habit of choosing the same route, thus freezing their behaviors. These results indicate that system behavior is much more complex and dynamic than implied by equilibrium analysis.
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40

Das, S. K. "Inductive Learning of Complex Fuzzy Relation." International Journal of Computer Science, Engineering and Information Technology 1, no. 5 (December 31, 2011): 29–38. http://dx.doi.org/10.5121/ijcseit.2011.1503.

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41

Feldman, Yotam M. Y., Mooly Sagiv, Sharon Shoham, and James R. Wilcox. "Learning the boundary of inductive invariants." Proceedings of the ACM on Programming Languages 5, POPL (January 4, 2021): 1–30. http://dx.doi.org/10.1145/3434296.

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42

Lee, Chang-Hwan. "Learning inductive rules using hellinger measure." Applied Artificial Intelligence 13, no. 8 (November 1999): 743–62. http://dx.doi.org/10.1080/088395199117207.

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43

Gentry, James A., Michael J. Shaw, Antoinette C. Tessmer, and David T. Whitford. "Using Inductive Learning to Predict Bankruptcy." Journal of Organizational Computing and Electronic Commerce 12, no. 1 (March 2002): 39–57. http://dx.doi.org/10.1207/s15327744joce1201_04.

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44

Miskovic, Vladislav. "Mathematical modeling of inductive machine learning." Vojnotehnicki glasnik 51, no. 1 (2003): 9–20. http://dx.doi.org/10.5937/vojtehg0301009m.

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45

Schwabacher, Mark, Thomas Ellman, and Haym Hirsh. "Inductive learning for engineering design optimization." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 10, no. 2 (April 1996): 179–80. http://dx.doi.org/10.1017/s0890060400001505.

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We are working on using machine learning to make the numerical optimization of complex engineering designs faster and more reliable. We envision a system that learns from previous design sessions knowledge that enables it to assist the engineer in setting up and carrying out a new design optimization. We have performed initial experiments for two aspects of setting up an optimization: selecting a prototype to serve as a starting point for the optimization and selecting a reformulation of the search space. Both choices can dramatically affect the speed and the reliability of design optimization.
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46

Fortes, I., L. Mora-López, R. Morales, and F. Triguero. "Inductive learning models with missing values." Mathematical and Computer Modelling 44, no. 9-10 (November 2006): 790–806. http://dx.doi.org/10.1016/j.mcm.2006.02.013.

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47

Aksoy, Mehmet Sabih, Gültekin Çağıl, and Ahmet Kürşat Türker. "Number-plate recognition using inductive learning." Robotics and Autonomous Systems 33, no. 2-3 (November 2000): 149–53. http://dx.doi.org/10.1016/s0921-8890(00)00085-3.

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48

Schank, Roger C., Gregg C. Collins, and Lawrence E. Hunter. "Transcending inductive category formation in learning." Behavioral and Brain Sciences 9, no. 4 (December 1986): 639–51. http://dx.doi.org/10.1017/s0140525x00051578.

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AbstractThe inductive category formation framework, an influential set of theories of learning in psychology and artificial intelligence, is deeply flawed. In this framework a set of necessary and sufficient features is taken to define a category. Such definitions are not functionally justified, are not used by people, and are not inducible by a learning system. Inductive theories depend on having access to all and only relevant features, which is not only impossible but begs a key question in learning. The crucial roles of other cognitive processes (such as explanation and credit assignment) are ignored or oversimplified. Learning necessarily involves pragmatic considerations that can only be handled by complex cognitive processes.We provide an alternative framework for learning according to which category definitions must be based on category function. The learning system invokes other cognitive processes to accomplish difficult tasks, makes inferences, analyses and decides among potential features, and specifies how and when categories are to be generated and modified. We also examine the methodological underpinnings of the two approaches and compare their motivations.
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49

Goldfarb, Lev. "A cognitive theory without inductive learning." Behavioral and Brain Sciences 15, no. 3 (September 1992): 446–47. http://dx.doi.org/10.1017/s0140525x00069569.

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50

Torkzadeh, Gholamreza, Krzysztof J. Cios, and Kurt A. Pflughoeft. "Inductive machine learning for instrument development." Information & Management 31, no. 1 (October 1996): 47–55. http://dx.doi.org/10.1016/s0378-7206(96)01061-0.

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