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A comparison of incremental case-based reasoning and inductive learning [chapter]

Barry Smyth, Pádraig Cunningham
1995 Lecture Notes in Computer Science  
The main contribution of this paper is an evaluation of how this incremental case-based reasoning compares with a pure inductive learning approach to the same task.  ...  This use of information theoretic techniques in CBR raises the question of whether a standard inductive learning approach would not solve this problem adequately.  ...  These information theoretic criteria bear the hallmarks of an inductive learning approach to the problem so in this paper we evaluate our incremental CBR by comparison with a pure inductive learning approach  ... 
doi:10.1007/3-540-60364-6_34 fatcat:rw5zba4n3veupj6ix5kdpsqlgy

Decoupling between causal understanding and awareness during learning and inference [article]

Jihyea Lee, Jerald D. Kralik, YuJin Cha, Suyeon Heo, Sang Wan Lee
2018 bioRxiv   pre-print
Causal reasoning is a principal higher-cognitive ability of humans, however, much remains unknown, including (a) the type (systematic versus intermixed) and order (inductive-then-deductive or vice versa  ...  Our findings clarify processes underlying causal reasoning, and reveal a complex relationship between causal reasoning and metacognitive awareness of it.  ...  Jee Hang Lee's assistance and insightful comments. All rights reserved. No reuse allowed without permission.  ... 
doi:10.1101/391938 fatcat:dp47yjnwtbfbfekrgztqunr474

Hybrid decision tree

Zhi-Hua Zhou, Zhao-Qian Chen
2002 Knowledge-Based Systems  
HDT simulates human reasoning by using symbolic learning to do qualitative analysis and using neural learning to do subsequent quantitative analysis.  ...  Moreover, this paper distinguishes three kinds of incremental learning tasks.  ...  Acknowledgements The National Natural Science Foundation of P.R.China and the Natural Science Foundation of Jiangsu Province, P.R.China, supported this research.  ... 
doi:10.1016/s0950-7051(02)00038-2 fatcat:ox57obgm2be2vmrix5msnygowa

INRECA: A seamlessly integrated system based on inductive inference and case-based reasoning [chapter]

E. Auriol, S. Wess, M. Manago, K. D. Althoff, R. Traphöner
1995 Lecture Notes in Computer Science  
This paper focuses on integrating inductive inference and case-based reasoning.  ...  We study integration along two dimensions: Integration of case-based methods with methods based on general domain knowledge, and integration of problem solving and incremental learning from experience.  ...  They also wish to thank the reviewers, whose the helpful comments contributed to make this paper much more readable and sound. 7.  ... 
doi:10.1007/3-540-60598-3_33 fatcat:q4qqdqza3bhhla54z4k4iqieau

An incremental decision tree learning methodology regarding attributes in medical data mining

Sam Chao, Fai Wong
2009 2009 International Conference on Machine Learning and Cybernetics  
Decision tree is one kind of inductive learning algorithms that offers an efficient and practical method for generalizing classification rules from previous concrete cases that already solved by domain  ...  Recently, many researches have been reported to endow decision trees with incremental learning ability, which is able to address the learning task with a stream of training instances.  ...  Acknowledgements The authors are grateful to the Faculty of Science and Technology of the University of Macau for supporting our research in various aspects.  ... 
doi:10.1109/icmlc.2009.5212333 fatcat:wlafhwa7ereifdwktjcg64w524

Incremental Induction of Classification Rules for Cultural Heritage Documents [chapter]

Teresa M. A. Basile, Stefano Ferilli, Nicola Di Mauro, Floriana Esposito
2004 Lecture Notes in Computer Science  
This work presents the application of a first-order logic incremental learning system, INTHELEX, to learn rules for the automatic identification of a wide range of significant document classes and their  ...  Incrementality plays a key role when the set of documents is continuously augmented.  ...  This work was partially funded by the EU project IST-1999-20882 COLLATE "Collaboratory for Annotation, Indexing and Retrieval of Digitized Historical Archive Material".  ... 
doi:10.1007/978-3-540-24677-0_94 fatcat:32xgy4b75vcbjpw5llfr256tsm

Integrating induction and case-based reasoning: Methodological approach and first evaluations [chapter]

Eric Auriol, Michel Manago, Klaus-Dieter Althoff, Stefan Wess, Stefan Dittrich
1995 Lecture Notes in Computer Science  
We propose in this paper a general framework for integrating inductive and case-based reasoning techniques for diagnosis tasks.  ...  We present a set of practical integrated approaches realised between the KATE-Induction decision tree builder and the PATDEX case-based reasoning system.  ...  The "CAR" domain comes from the UCI Repository of Machine Learning Data Bases and Domain Theories, U.S.A.  ... 
doi:10.1007/3-540-60364-6_24 fatcat:butpwngdorac7lmlpq56coqf3i

Handling Continuous-Valued Attributes in Incremental First-Order Rules Learning [chapter]

Teresa M. A. Basile, Floriana Esposito, Nicola Di Mauro, Stefano Ferilli
2005 Lecture Notes in Computer Science  
Experiments were carried out on a real-world domain and a comparison with a state-of-art system is reported.  ...  In this work we present a strategy to handle such information in a relational learning incremental setting and its integration with classical symbolic approaches to theory revision.  ...  In these cases incremental learning, as opposed to batch learning, is needed.  ... 
doi:10.1007/11558590_43 fatcat:jcxdhkfe75chlbnr3uge45ikmi

Integrating Non-monotonic Logical Reasoning and Inductive Learning With Deep Learning for Explainable Visual Question Answering

Heather Riley, Mohan Sridharan
2019 Frontiers in Robotics and AI  
It also incrementally learns and reasons with previously unknown constraints governing the domain's states.  ...  As a motivating example of a task that requires explainable reasoning and learning, we consider Visual Question Answering in which, given an image of a scene, the objective is to answer explanatory questions  ...  In this paper, we have introduced a new component for incrementally learning constraints governing domain states, expanded reasoning to support planning and diagnostics, and discussed more detailed experimental  ... 
doi:10.3389/frobt.2019.00125 pmid:33501140 pmcid:PMC7805953 fatcat:t7i5yif6wreendws6tvzoflevm

Page 7911 of Mathematical Reviews Vol. , Issue 99k [page]

1999 Mathematical Reviews  
Finally, it is argued that these results support an incremental view of reasoning in a natural way, and the notion of relevance to the environment, captured by the learning to reason framework, is discussed  ...  a useful way to integrate learning with reasoning.  ... 

On Symmetry and Quantification: A New Approach to Verify Distributed Protocols

Aman Goel, Karem A. Sakallah
2021 Zenodo  
of clause learning during incremental induction.  ...  Our approach is implemented in IC3PO, a new verifier for distributed protocols that significantly outperforms the state-of-the-art, scales orders of magnitude faster, and robustly derives compact inductive  ...  Appendix D Effect of Symmetry Learning in Incremental Induction This section evaluates the effect of symmetry-aware clause boosting in finitedomain incremental induction with a detailed comparison between  ... 
doi:10.5281/zenodo.4641704 fatcat:ln5my5563fce7c52pcp2ilsklu

Non-monotonic Logical Reasoning Guiding Deep Learning for Explainable Visual Question Answering [article]

Heather Riley, Mohan Sridharan
2019 arXiv   pre-print
It also incrementally learns and reasons with previously unknown constraints governing the domain's states.  ...  Towards addressing these limitations, our architecture draws inspiration from research in cognitive systems, and integrates the principles of commonsense logical reasoning, inductive learning, and deep  ...  Data Availability Statement The datasets generated or analyzed for this study, and the software implementation of the architecture and algorithms, can be found in the following online repository: https  ... 
arXiv:1909.10650v1 fatcat:o3xgh77z6fh3vjnome6os2klka

Incremental learning of probabilistic rules from clinical databases based on rough set theory

S Tsumoto, H Tanaka
1997 Proceedings : a conference of the American Medical Informatics Association. AMIA Fall Symposium  
However, most of the approaches induce rules from all the data in databases and cannot induce incrementally when new samples are derived.  ...  In this paper, a new approach to knowledge acquisition, which induce probabilistic rules incrementally by using rough set technique, is introduced and was evaluated on two clinical databases.  ...  incremental learning methods as ordinry leaming algorithms, we introduce an inemental leining mehod PRIMEROSE-NC(Prbabilistic Rule Induction Method based on Rough Sets for Incremental Leaing Methods).  ... 
pmid:9357616 pmcid:PMC2233251 fatcat:nzkwj6335zdjfbr3fm3fnlpdvm

Incremental Learning in Inductive Programming [chapter]

Robert Henderson
2010 Lecture Notes in Computer Science  
with and without incremental learning.  ...  Using a search-based inductive functional programming system modelled on the MagicHaskeller system of Katayama (2007) , we perform a set of experiments comparing the performance of inductive programming  ...  Acknowledgments Thank you to my MSc supervisor Michael O'Boyle, for his support and encouragement on this project.  ... 
doi:10.1007/978-3-642-11931-6_4 fatcat:7fikd3cfkbb2xn5mgxtulozo7q

Learning Adaptive Propagation for Knowledge Graph Reasoning [article]

Yongqi Zhang, Zhanke Zhou, Quanming Yao, Xiaowen Chu, Bo Han
2022 arXiv   pre-print
Extensive experiments show that our method is efficient and achieves state-of-the-art performances in both transductive and inductive reasoning settings, benefiting from the deeper propagation.  ...  Due to the success of Graph Neural Networks (GNNs) in learning from graph-structured data, various GNN-based methods have been introduced to learn from knowledge graphs (KGs).  ...  We further provide a comparison of the straight-through estimator and REINFORCE estimator in Appendix B.4. Overall, the incremental sampling equipped with the learned distribution works the best.  ... 
arXiv:2205.15319v1 fatcat:qqjyrz45wrcu5agl7oswnh3qyi
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