Relevant bibliographies by topics / Inductive

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Inductive'

Author: Grafiati
Published: 4 June 2021
Last updated: 30 January 2023

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Journal articles on the topic "Inductive"

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He, Li Ping, Zong Zhang Chen, and Yiu Wing Mai. "Induction Ability of CAA and Anti-Corrosion Property of Inductively Formed Apatite/Al2O3." Advanced Materials Research 41-42 (April 2008): 75–80. http://dx.doi.org/10.4028/www.scientific.net/amr.41-42.75.

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Ca-containing anodic alumina (CAA) has been successfully prepared by anodizing Al film in an alkali solution at a constant voltage and subsequently electro-depositing calcium salts on and into porous anodic alumina. This paper studied the induction ability of Ca-containing anodic alumina (CAA) for calcium phosphates salts by immersing CAA in a simulated body fluid (SBF). The morphologies and compositions of the inductive coating are studied in depth using SEM and XRD. The results show that the porous Ca-containing anodic alumina (CAA) exhibits good induction ability of calcium phosphates in SBF. The Ca/P atomic ratio of the inductive coating on CAA after 7 days immersion in SBF is of 1.68 and the inductive coating on CAA is apatite. Consequently apatite /Al2O3 (anodic) composite has been obtained after apatite inductively formed on Ca-containing anodic alumina. Tafel polarization test indicates that apatite /Al2O3 composite has good anti-corrosion ability in simulated body environment. Therefore, Ca-containing anodic alumina films are promising substrates for fabricating functional coatings and its inductively formed apatite/Al2O3 composite is a promising material for hard tissue repair applications.
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Sterkenburg, Tom F. "THE META-INDUCTIVE JUSTIFICATION OF INDUCTION." Episteme 17, no. 4 (February 7, 2019): 519–41. http://dx.doi.org/10.1017/epi.2018.52.

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ABSTRACTI evaluate Schurz's proposed meta-inductive justification of induction, a refinement of Reichenbach's pragmatic justification that rests on results from the machine learning branch of prediction with expert advice.My conclusion is that the argument, suitably explicated, comes remarkably close to its grand aim: an actual justification of induction. This finding, however, is subject to two main qualifications, and still disregards one important challenge.The first qualification concerns the empirical success of induction. Even though, I argue, Schurz's argument does not need to spell out what inductive method actually consists in, it does need to postulate that there is something like the inductive or scientific prediction strategy that has so far been significantly more successful than alternative approaches. The second qualification concerns the difference between having a justification for inductive method and for sticking with induction for now. Schurz's argument can only provide the latter. Finally, the remaining challenge concerns the pool of alternative strategies, and the relevant notion of a meta-inductivist's optimality that features in the analytic step of Schurz's argument. Building on the work done here, I will argue in a follow-up paper that the argument needs a stronger dynamic notion of a meta-inductivist's optimality.
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Yagola, G. K., D. R. Vasil'ev, and Yu I. Kazantsev. "Hyperconducting inductive measure of magnetic induction." Measurement Techniques 28, no. 5 (May 1985): 401–4. http://dx.doi.org/10.1007/bf00864929.

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Buntine, Wray. "Inductive knowledge acquisition and induction methodologies." Knowledge-Based Systems 2, no. 1 (March 1989): 52–61. http://dx.doi.org/10.1016/0950-7051(89)90008-7.

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Zhang, Xiuren, David G. Himelrick, Floyd M. Woods, and Robert C. Ebel. "Effect of Temperature, Photoperiod, and Pretreatment Growing Condition on Floral Induction in Spring-bearing Strawberry." HortScience 35, no. 4 (July 2000): 556B—556a. http://dx.doi.org/10.21273/hortsci.35.4.556b.

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`Chandler' strawberry plants (Fragaria Xananassa Duch.) were greenhouse grown under natural lighting and then placed into growth chambers at two constant temperatures of 16 and 26 °C and 2 daylengths of 9 h (SD) and 9-h photoperiod (NI) which was night interrupted with 3 hours of incandescent radiation at 30-45 μmol·s-1·m-2 PAR. Plants were given different numbers of inductive cycles in growth chambers and then moved to the greenhouse. Flowering and growth were monitored. Flowering was completely inhibited at 26 °C, regardless of pretreatment growing conditions such as pot sizes and plant ages, photoperiod, and inductive cycles. At 16 °C, SD promoted floral induction compared to NI under all inductive cycles except a 7-day induction. The minimum number of inductive cycles required at 16 °C for floral induction was dependent on photoperiod and prior greenhouse treatment. Flowering rate was also affected by greenhouse treatment, photoperiod, and inductive cycles. Runner production was affected by photoperiod and temperature × inductive cycle.
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Kaposi, Ambrus, András Kovács, and Thorsten Altenkirch. "Constructing quotient inductive-inductive types." Proceedings of the ACM on Programming Languages 3, POPL (January 2, 2019): 1–24. http://dx.doi.org/10.1145/3290315.

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Vakarelov, Dimiter. "Inductive Modal Logics1." Fundamenta Informaticae 16, no. 3-4 (May 1, 1992): 383–405. http://dx.doi.org/10.3233/fi-1992-163-411.

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The main aim of this paper is to study inductive modal logics – bi-modal logics, containing the Segerberg’s induction axiom Seg, known from PDL. An adequate semantical characterization of Seg is given, and completeness theorems for a number of inductive modal logics are proved by a generalization of Segerberg’s filtration from PDL.
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Misra, Kanishka. "On Semantic Cognition, Inductive Generalization, and Language Models." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 11 (June 28, 2022): 12894–95. http://dx.doi.org/10.1609/aaai.v36i11.21584.

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My doctoral research focuses on understanding semantic knowledge in neural network models trained solely to predict natural language (referred to as language models, or LMs), by drawing on insights from the study of concepts and categories grounded in cognitive science. I propose a framework inspired by 'inductive reasoning,' a phenomenon that sheds light on how humans utilize background knowledge to make inductive leaps and generalize from new pieces of information about concepts and their properties. Drawing from experiments that study inductive reasoning, I propose to analyze semantic inductive generalization in LMs using phenomena observed in human-induction literature, investigate inductive behavior on tasks such as implicit reasoning and emergent feature recognition, and analyze and relate induction dynamics to the learned conceptual representation space.
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KUSAKARI, K. "Primitive Inductive Theorems Bridge Implicit Induction Methods and Inductive Theorems in Higher-Order Rewriting." IEICE Transactions on Information and Systems E88-D, no. 12 (December 1, 2005): 2715–26. http://dx.doi.org/10.1093/ietisy/e88-d.12.2715.

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Nedeljković, Mitar. "The problem of justifying inductive reasoning." Zbornik radova Filozofskog fakulteta u Pristini 51, no. 2 (2021): 387–408. http://dx.doi.org/10.5937/zrffp51-30620.

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In this paper, the author considers the classical strategies of defense from Hume's argument against induction, and assesses the extent to which they were found to be successful. Synthetic, linguistic, a priori, pragmatic, and inductive strategies of defending induction are considered, as well as the question of the extent to which the justification of induction is a problem for grounding scientific knowledge. A new argument is introduced for the a priori justification of induction, as well as a critique of the synthetic and inductive defenses of induction by Black and Jacquette.
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Dissertations / Theses on the topic "Inductive"

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Forsberg, Fredrik Nordvall. "Inductive-inductive definitions." Thesis, Swansea University, 2013. https://cronfa.swan.ac.uk/Record/cronfa43083.

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The principle of inductive-inductive definitions is a principle for defining data types in Martin-Lof Type Theory. It allows the definition of a set A, simultaneously defined w ith a family B : A → Set indexed over A. Such forms of definitions have been used by several authors in order to for example define the syntax of Type Theory in Type Theory itself. This thesis gives a theoretical justification for their use. We start by giving a finite axiomatisation of a type theory with inductive-inductive definitions in the style of Dybjer and Setzer's axiomatisation of inductive-recursive definitions. We then give a categorical characterisation of inductive-inductive definitions as initial objects in a certain category. This is presented using a general framework for elimination rules based on the concept of a Category with Families. To show consistency of inductive-inductive definitions, a set-theoretical model is constructed. Furthermore, we give a translation of our theory with a simplified form of the elimination rule into the already existing theory of indexed inductive definitions. This translation does not seem possible for the general elimination rule. Extensions to the theory are investigated, such as a combined theory of inductive-inductive-recursive definitions, more general forms of indexing and arbitrarily high (finite) towers of inductive-inductive definitions. Even so, not all uses of inductive-inductive definitions in the literature (in particular the syntax of Type Theory) are covered by the theories presented. Finally, two larger, novel case studies of the use of inductive-inductive definitions are presented: Conway's Surreal numbers and a formalisation of positive inductive-recursive definitions.
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Dijkstra, Gabe. "Quotient inductive-inductive definitions." Thesis, University of Nottingham, 2017. http://eprints.nottingham.ac.uk/42317/.

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In this thesis we present a theory of quotient inductive-inductive definitions, which are inductive-inductive definitions extended with constructors for equations. The resulting theory is an improvement over previous treatments of inductive-inductive and indexed inductive definitions in that it unifies and generalises these into a single framework. The framework can also be seen as a first approximation towards a theory of higher inductive types, but done in a set truncated setting. We give the type of specifications of quotient inductive-inductive definitions mutually with its interpretation as categories of algebras. A categorical characterisation of the induction principle is given and is shown to coincide with the property of being an initial object in the categories of algebras. From the categorical characterisation of induction, we derive a more type theoretic induction principle for our quotient inductive-inductive definitions that looks like the usual induction principles. The existence of initial objects in the categories of algebras associated to quotient inductive-inductive definitions is established for a class of definitions. This is done by a colimit construction that can be carried out in type theory itself in the presence of natural numbers, sum types and quotients or equivalently, coequalisers.
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KUSAKARI, Keiichirou, Masahiko SAKAI, and Toshiki SAKABE. "Primitive Inductive Theorems Bridge Implicit Induction Methods and Inductive Theorems in Higher-Order Rewriting." IEICE, 2005. http://hdl.handle.net/2237/9580.

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Hill, Alexandra. "Reasoning by analogy in inductive logic." Thesis, University of Manchester, 2013. https://www.research.manchester.ac.uk/portal/en/theses/reasoning-by-analogy-in-inductive-logic(039622d8-ab3f-418f-b46c-4d4e7a9eb6c1).html.

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This thesis investigates ways of incorporating reasoning by analogy into Pure (Unary) Inductive Logic. We start with an analysis of similarity as distance, noting that this is the conception that has received most attention in the literature so far. Chapter 4 looks in some detail at the consequences of adopting Hamming Distance as our measure of similarity, which proves to be a strong requirement. Chapter 5 then examines various adaptations of Hamming Distance and proposes a subtle modification, further-away-ness, that generates a much larger class of solutions.
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Lindblom, Adam. "Inductive Pulse Generation." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis : Univ.-bibl. [distributör], 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-6699.

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Zebulske, Terry E. "Inductive Bible study methodology." Theological Research Exchange Network (TREN), 1988. http://www.tren.com.

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Kehris, Evangelos. "Incremental inductive interactive simulation." Thesis, Lancaster University, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.302936.

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Ray, Oliver. "Hybrid abductive inductive learning." Thesis, Imperial College London, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.428111.

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Davies, Winton H. E. "Communication of inductive inference." Thesis, University of Aberdeen, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.400670.

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This thesis addresses the question: "How can knowledge learnt through inductive inference be communicated in a multi-agent system?". Existing agent communication languages, such as KQML, assume logically sound inference methods. Unfortunately, induction is logically unsound. In general, machine learning techniques infer knowledge (or hypotheses) consistent with the locally available facts. However, in a multi-agent system, hypotheses learnt by one agent can directly contradict knowledge held by another. If an agent communicates induced knowledge as though it were logically sound, then the knowledge held by other agents in the community may become inconsistent. The answer we present in this thesis is that agents must, in general, communicate the bounds to such induced knowledge. The Version Space framework characterises inductive inference as a process which identifies the set of hypotheses that are consistent with both the observable facts and the constraints of the hypothesis description language. A Version Space can be expressed by two boundary sets, which represent the most general and most specific hypotheses. We thus propose that when communicating an induced hypothesis, that the hypothesis be bounded by descriptions of the most general and most specific hypotheses. In order to allow agents to integrate induced hypotheses with their own facts or their own induced hypotheses, the technique of Version Space Intersection can be used. We have investigated how boundary set descriptions can be generated for the common case of machine learning algorithms which learn hypotheses from unrestricted Version Spaces. This is a hard computational problem, as it is the equivalent of finding the minimal DNF description of a set of logical sentences. We consider four alternate approaches: exact minimization using the Quine-McCluskey algorithm; a naive, information-theoretic hill-climbing search; Espresso II, a sophisticated, heuristic logic minimization algorithm; and unsound approximation techniques. We demonstrate that none of these techniques are scalable to realistic machine learning problems.
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Pascoe, James. "The evoluation of 'Boxes' to quantized inductive learning : a study in inductive learning /." Thesis, This resource online, 1996. http://scholar.lib.vt.edu/theses/available/etd-12172008-063016/.

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Books on the topic "Inductive"

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Gabbay, Dov M. Inductive Logic. San Diego: North Holland [Imprint], 2011.

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Van Schuylenbergh, Koenraad, and Robert Puers, eds. Inductive Powering. Dordrecht: Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-90-481-2412-1.

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1927-, Keller George Vernon, ed. Inductive mining prospecting. Amsterdam: Elsevier, 1985.

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Stephen, Muggleton, ed. Inductive logic programming. London: Academic Press in association with Turing Institute Press, 1992.

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Katzouris, Nikos, and Alexander Artikis, eds. Inductive Logic Programming. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97454-1.

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Riguzzi, Fabrizio, and Filip Železný, eds. Inductive Logic Programming. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38812-5.

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Riguzzi, Fabrizio, Elena Bellodi, and Riccardo Zese, eds. Inductive Logic Programming. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99960-9.

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Cussens, James, and Alan Frisch, eds. Inductive Logic Programming. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-44960-4.

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Davis, Jesse, and Jan Ramon, eds. Inductive Logic Programming. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23708-4.

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Cussens, James, and Alessandra Russo, eds. Inductive Logic Programming. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63342-8.

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Book chapters on the topic "Inductive"

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Nordvall Forsberg, Fredrik, and Anton Setzer. "Inductive-Inductive Definitions." In Computer Science Logic, 454–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15205-4_35.

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Day, John Patrick. "Inductive Reasoning and Inductive Logic." In Inductive Probability, 278–322. London: Routledge, 2021. http://dx.doi.org/10.4324/9781003244356-7.

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Altenkirch, Thorsten, Paolo Capriotti, Gabe Dijkstra, Nicolai Kraus, and Fredrik Nordvall Forsberg. "Quotient Inductive-Inductive Types." In Lecture Notes in Computer Science, 293–310. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-89366-2_16.

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Anthony, Simon, and Alan M. Frisch. "Cautious induction in inductive logic programming." In Inductive Logic Programming, 45–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3540635149_34.

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Falke, Stephan, and Deepak Kapur. "Inductive Decidability Using Implicit Induction." In Logic for Programming, Artificial Intelligence, and Reasoning, 45–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11916277_4.

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Day, John Patrick. "Inductive Probabilification by Elimination." In Inductive Probability, 226–77. London: Routledge, 2021. http://dx.doi.org/10.4324/9781003244356-6.

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Day, John Patrick. "Functional Inductive Probabilification-Judgement-Formulas." In Inductive Probability, 142–58. London: Routledge, 2021. http://dx.doi.org/10.4324/9781003244356-4.

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Day, John Patrick. "Other Alleged Determinants of Inductive Probability." In Inductive Probability, 159–225. London: Routledge, 2021. http://dx.doi.org/10.4324/9781003244356-5.

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Day, John Patrick. "Subject-Predicate Inductive Probabilification-Judgement-Formulas." In Inductive Probability, 83–141. London: Routledge, 2021. http://dx.doi.org/10.4324/9781003244356-3.

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Day, John Patrick. "The Philosophical Problem of Inductive Probability." In Inductive Probability, 1–82. London: Routledge, 2021. http://dx.doi.org/10.4324/9781003244356-2.

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Conference papers on the topic "Inductive"

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Sato, Haruhiko, and Masahito Kurihara. "Discovering inductive theorems using rewriting induction." In 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2016. http://dx.doi.org/10.1109/smc.2016.7844370.

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Nagashima, Yutaka. "Faster Smarter Proof by Induction in Isabelle/HOL." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/273.

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We present sem_ind, a recommendation tool for proof by induction in Isabelle/HOL. Given an inductive problem, sem_ind produces candidate arguments for proof by induction, and selects promising ones using heuristics. Our evaluation based on 1,095 inductive problems from 22 source files shows that sem_ind improves the accuracy of recommendation from 20.1% to 38.2% for the most promising candidates within 5.0 seconds of timeout compared to its predecessor while decreasing the median value of execution time from 2.79 seconds to 1.06 seconds.
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Kovács, András, and Ambrus Kaposi. "Large and Infinitary Quotient Inductive-Inductive Types." In LICS '20: 35th Annual ACM/IEEE Symposium on Logic in Computer Science. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3373718.3394770.

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Miyamoto, S. "Inductive and non-inductive methods of clustering." In 2012 IEEE International Conference on Granular Computing (GrC-2012). IEEE, 2012. http://dx.doi.org/10.1109/grc.2012.6468710.

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Aoto, Takahito, and Sorin Stratulat. "Decision Procedures for Proving Inductive Theorems without Induction." In the 16th International Symposium. New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2643135.2643156.

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Olsen, Dan R., and Xinyu Deng. "Inductive groups." In the 9th annual ACM symposium. New York, New York, USA: ACM Press, 1996. http://dx.doi.org/10.1145/237091.237120.

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Gevorkyan, G. S., R. G. Manucharyan, V. M. Mekhitarian, A. M. Razhev, I. M. Ulanov, and K. N. Kolmakov. "Inductive lasers." In SPIE Proceedings, edited by Guenter Huber, Vladislav Y. Panchenko, and Ivan A. Scherbakov. SPIE, 2005. http://dx.doi.org/10.1117/12.660802.

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Bradley, Aaron R., and Zohar Manna. "Checking Safety by Inductive Generalization of Counterexamples to Induction." In Formal Methods in Computer Aided Design (FMCAD'07). IEEE, 2007. http://dx.doi.org/10.1109/fmcad.2007.4401997.

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Bradley, Aaron R., and Zohar Manna. "Checking Safety by Inductive Generalization of Counterexamples to Induction." In Formal Methods in Computer Aided Design (FMCAD'07). IEEE, 2007. http://dx.doi.org/10.1109/famcad.2007.15.

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Wessel, F. J., N. Bolte, V. Kiyashko, M. Morehouse, T. Roche, and M. Slepchenkov. "Pulsed-inductive thruster." In 2013 IEEE 25th Symposium on Fusion Engineering (SOFE). IEEE, 2013. http://dx.doi.org/10.1109/sofe.2013.6635499.

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Reports on the topic "Inductive"

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Greiner, John. Programming with Inductive and Co-Inductive Types. Fort Belvoir, VA: Defense Technical Information Center, January 1992. http://dx.doi.org/10.21236/ada249562.

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Miller, R. B., B. B. Davis, and J. Bayless. Inductive Adder development. Office of Scientific and Technical Information (OSTI), May 1989. http://dx.doi.org/10.2172/10191277.

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Griffiths, Thomas. Fast, Flexible, Rational Inductive Inference. Fort Belvoir, VA: Defense Technical Information Center, August 2013. http://dx.doi.org/10.21236/ada590143.

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Kaplan, Steven L. Summary of Inductive SiC BJT Switching. Fort Belvoir, VA: Defense Technical Information Center, November 2005. http://dx.doi.org/10.21236/ada440925.

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Cobb, Helen G. Inductive Biases in a Reinforcement Learner,. Fort Belvoir, VA: Defense Technical Information Center, July 1992. http://dx.doi.org/10.21236/ada294127.

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Nair, Lakshmi S., David Rowe, Peter Maye, and Douglas Adams. Inductive Microenvironment for Improved Osseous Integration. Fort Belvoir, VA: Defense Technical Information Center, October 2012. http://dx.doi.org/10.21236/ada578636.

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Barry, W. Inductive megahertz beam position monitors for CEBAF. Office of Scientific and Technical Information (OSTI), January 1989. http://dx.doi.org/10.2172/6360210.

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Kinsey, J., and D. A. Ehst. Inductive currents in an rf driven plasma. Office of Scientific and Technical Information (OSTI), August 1991. http://dx.doi.org/10.2172/5218308.

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Gupta, Aarti, and Allan L. Fisher. Representation and Manipulation of Inductive Boolean Functions. Fort Belvoir, VA: Defense Technical Information Center, April 1992. http://dx.doi.org/10.21236/ada256078.

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Altshuler, Edward. An Electrically Small Inductive-Loaded Planar Antenna. Fort Belvoir, VA: Defense Technical Information Center, February 2009. http://dx.doi.org/10.21236/ada510475.

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