Inductive learning system learns classification from training examples and uses induced rules for classifying new instances. If a decision cannot be inferred from system rule base, a default rule is usually applied. In the paper a new interactive approach is proposed where in uncertain conditions an interactive inductive learning system can ask for human decision and improve its knowledge base with the rule derived from this decision. Problems and solutions of incorporation of human-made decision into rule base and aspects of choosing between static and incremental learning algorithms are analyzed in context of the proposed approach. An interactive inductive learning system is proposed to assist in study course comparative analysis.

Cited by 3

Ensemble Inductive Transfer Learning

Xiaobo Liu, Guangjun Wang, Zhihua Cai null et al. · Journal of Fiber Bioengineering and Informatics · 2015

Inductive transfer learning is a major research area in transfer learning which aims at achieving a high performance in the target domain by inducing the useful knowledge from the source domain. By combining decisions from individual classifiers, ensemble learning can usually reduce variance and achieve higher accuracy than a single classifier. In this paper, we propose a novel Ensemble Inductive Transfer Learning (EITL) method. EITL builds a set of classifiers by recording the iterative process of knowledge transfer. In each iteration, it uses the classifier of the source domain, the base classifier of the target domain built on the initial labeled data, and the most recent classifier built on the updated labeled data, to classify unlabeled instances, and add some self-labeled instances to the labeled data, and then trains a new classifier. At the end, all the classifiers built in this process are used for classification. We conduct experiments on synthetic data sets and six UCI data sets, which show that EITL is an effective algorithm in terms of classification accuracy.

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Inductive Learning Replication Experiment

Kara Skorge, Keely Stevenson, William Dalby · OSF Preprints (OSF Preprints)

Link to the experiment: https://replicationkornellbjork.formr.org Link to pre-registration plan: https://aspredicted.org/xn5i9.pdf

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Simulation-assisted inductive learning

Bruce G. Buchanan, John Sullivan, Tze-Pin Cheng et al. · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · 1988

Learning by induction can require a large number of training examples. We show the power of using a simulator to generate training data and test data in learning rules for an expert system. The induction program is RL, a simplified version of Meta-DENDRAL. The expert system is ABLE, a rule-based system that identifies and located errors in particle beam lines used in high energy physics. A simulator of beam lines allowed forming and testing rules on sufficient numbers of cases that ABLE's performance is demonstrably accurate and precise. 13 refs., 2 figs.

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Interactive Inductive Learning System

Ilze Birzniece · 2011

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Limitations On Inductive Learning

Exte Nd Ed, Thomas G. Dietterich · 1997

) Thomas G. Dietterich Department of Computer Science Oregon State University Corvallis, OR 97331 tgd@cs.orst.edu ABSTRACT This paper explores the proposition that inductive learning from examples is fundamentally limited to learning only a small fraction of the total space of possible hypotheses. We begin by defining the notion of an algorithm reliably learning a good approximation to a concept C. An empirical study of three algorithms (the classical algorithm for maximally specific conjunctive generalizations, ID3, and back-propagation for feed-forward networks of logistic units) demonstrates that each of these algorithms performs very poorly for the task of learning concepts defined over the space of Boolean feature vectors containing 3 variables. Simple counting arguments allow us to prove an upper bound on the maximum number of concepts reliably learnable from m training examples. INTRODUCTION How good are current inductive learning algorithms? How well can any inductive ...

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Nonmonotonic abductive inductive learning

Oliver Ray · Journal of Applied Logic · 2008

Cited by 147

Inductive Supervised Quantum Learning

Alex Monràs, Gael Sentís, Péter Wittek · Physical Review Letters · 2017

In supervised learning, an inductive learning algorithm extracts general rules from observed training instances, then the rules are applied to test instances. We show that this splitting of training and application arises naturally, in the classical setting, from a simple independence requirement with a physical interpretation of being nonsignaling. Thus, two seemingly different definitions of inductive learning happen to coincide. This follows from the properties of classical information that break down in the quantum setup. We prove a quantum de Finetti theorem for quantum channels, which shows that in the quantum case, the equivalence holds in the asymptotic setting, that is, for large numbers of test instances. This reveals a natural analogy between classical learning protocols and their quantum counterparts, justifying a similar treatment, and allowing us to inquire about standard elements in computational learning theory, such as structural risk minimization and sample complexity.

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Learning and Inductive Inference

Dietterich, Thomas G., London, Bob, Clarkson, Kenneth et al. · Defense Technical Information Center (DTIC) · 1982

The main results of the report are: (a) a simple model that serves to generate a taxonomy of learning systems, (b) the explication and clarification of four methods of learning single concepts, (c) some understanding of the underlying causes of the credit-assignment problem (and possible solutions), and (d) some identification of open research problems and areas that have received little attention. The report is structured as a set of articles. Seven of the articles present the main problems and issues in learning research, while the remaining fifteen articles describe particular learning systems that have been developed.

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INDUCTIVE LEARNING WITH BCT

Philip K. Chan · Elsevier eBooks · 1989

Cited by 13

Learning by Inductive Inference

Ryszard S. Michalski · George Mason University · 1976

Cited by 11Open access

Inductive Learning Replication Experiment

William Dalby, Kara Skorge, Keely Stevenson · OSF Preprints (OSF Preprints) · 2021

Link to the experiment: https://replicationkornellbjork.formr.org Link to pre-registration plan: https://aspredicted.org/xn5i9.pdf

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Hybrid Abductive Inductive Learning

Ray, Oliver · OpenGrey (Institut de l'Information Scientifique et Technique) · 2005

This thesis introduces a new Machine Learning technique called Hybrid Abductive Inductive Learning (HAIL) that integrates Abductive Logic Programming (ALP) and Inductive Logic Programming (ILP) in order to automate the learning of first-order theories from examples and prior knowledge. A semantics is proposed called Kernel Set Subsumption (KSS) that generalises the well-known inference method of Bottom Generalisation by deriving hypotheses with more than one clause. A corresponding proof procedure is presented, called HAIL, which extends the ALP procedure of Kakas and Mancarella and integrates it within a generalisation of Muggleton’s widely-used ILP system Progol5. HAIL is shown to overcome some of the limitations of Progol5 — including a previously unsuspected incompleteness — and to enlarge the class of learning problems soluble in practice. i Acknowledgements I am especially grateful to my supervisors Alessandra Russo and Krysia Broda for their generous support, guidance and patience throughout this research. Special thanks also go to Antonis Kakas and Stephen Muggleton for several stimulating and fruitful discussions. I am grateful to Dov Gabbay and Christopher Hogger for their inspiration and encouragement. I would also like to mention Francesca Toni, Simon Colton and Artur Garcez for their helpful advice and Gerson Zaverucha for his useful comments regarding ancestor resolution. Discussions with Gordon Plotkin and Robin Smith helped to improve the historical accuracy of the work. I thank my family for their many sacrifices that made this work possible. Sasivimol Kittivoravitkul was my constant companion through the highs and lows of this research. I gladly acknowledge the financial support of the EPSRC and the Department of Computing at Imperial College. I extend my appreciation to the examiners Peter Flach and Murray Shanahan for their close reading of the thesis and valuable suggestions for improvement. Thanks are also due to the anonymous reviewers of [75, 74, 73, 72] for their useful comments.

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An Inductive Learning Approach

Filipe Alves Neto Verri · Open MIND · 2026

Changes in this version: Project restructuring Reorganized source files into chapters/, frontmatter/, and backmatter/ directories. Created custom document class dspbook.cls (based on memoir, A5 twoside). Removed planning.tex and courses.tex. Updated main.tex to use the new structure. Updated Makefile with new build targets. Companion slides Created full slide sets for all chapters and the mathematical foundations appendix. Metropolis theme, 16:9, grayscale, matching book fonts (STIXTwo/CourierPrime). TikZ figures reproduced from the book. Added slides/preamble.tex shared preamble and slides/README.md guidelines. Added Makefile targets: slides, slides-watch, slides-clean, and per-chapter builds. Content Significantly expanded the validation chapter (hypothesis testing, confidence intervals, statistical comparison of models). Added discussion of Large Language Models and attention mechanisms in the history chapter. Improved project chapter with methodology refinements and clarifications. Added comments on split invariance and data leakage in the data handling chapter. Updated mathematical foundations appendix. Grammar and fluency fixes across all eight chapter files, including: Fixed factual error in F-score description (higher beta gives more weight to recall, not precision). Fixed sample variance summation upper limit. Fixed ChatGPT release year (2020 to 2022). Removed stray backticks in learning chapter. Corrected typos, articles, subject-verb agreement, and punctuation throughout. Standardized gender-neutral language (spokesman to spokesperson). Bug fixes Fixes #4: Definition 5.5 (Disjoint tables) corrected. Fixes #5: Table 4.11 data corrected to molten weather. Fixes #6: Added missing nycflights13 citation. Fixes #7: Bind definition now states that T0 and T1 are disjoint. Fixes #8: Grade domain no longer includes ? by definition. Fixes #9: Sprint retrospective duplicate definition removed. Fixes #10: Label in data classification is now called class. Fixes #11: Cited John Tukey in modern data analysis history. Fixes #13: Annotated tables in data handling chapter with keys. Fixes #14: Included Modin and LARA in algebra discussion. Fixes #15: Inclusion is now described as a relation, not an operation. Other Added CLAUDE.md project instructions. Updated references.bib with new references. Updated glossary with new terms.

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