A Theory and Methodology of Inductive Learning

By Ryszard S. Michalski. 1983

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI (2019, positive): “This notion of model comprehensibility stems from the postulates of Michalski [22] , which stated that “the results of computer induction should be symbolic descriptions of given entities, semantically and structurally similar to those a human expert might produce observing the same entities.”
Inductive Logic Programming: Theory and Methods (1994, methodology): “As a matter of fact, the use of relations to express background knowledge, when learning concepts was already present in the Induce algorithm of [72], and in the ML-Smart system of [9].”
Extracting comprehensible models from trained neural networks (1996, background): “…their hypotheses as decision trees (Breiman et al., 1984; Quinlan, 1993), decision lists (Rivest, 1987; Clark & Niblett, 1989), inference rules (Michalski, 1983; Quinlan, 1993), neural networks (Rumelhart et al., 1986), hidden Markov models (Rabiner, 1989), Bayesian networks (Heckerman, 1995),…”
Connectionist Theory Refinement: Genetically Searching the Space of Network Topologies (1997, unknown): “Before presenting our new algorithm (Regent), we give an overview of the Kbann algorithm as well as our initial approach of re(cid:12)ning a Kbann-created network's topology (TopGen).”
Detecting concept drift in data streams using model explanation (2018, background): “The notion of comprehensibility (Michalski, 1983) is therefore the rationale behind incorporating means of explanation into the machine learning process(2).”
Using Sampling and Queries to Extract Rules from Trained Neural Networks (1994, background): “The add-value operator may either add another possible value for a feature already in the antecedent set (i.e., create an internal disjunction, Michalski, 1983), or add a feature that is not yet represented.”
Explanation-Based Generalization: A Unifying View (1986, result): “This is in marked contrast with similarity-based generalization methods (e.g. Michalski, 1983; Quinlan, 1985).”
Comprehensibility of Classification Trees–Survey Design (2019, background): “Its importance has been stressed since the early days of machine learning research [17, 19].”
On the Equivalence between Positional Node Embeddings and Structural Graph Representations (2019, background): “, 2004) and inductive learning (Michalski, 1983), (Belkin et al.”
Managing computational complexity using surrogate models: a critical review (2020, methodology): “In this method, the necessary rules are derived from examples; these rules are used to divide the data into distinct classes and are the principal modeling concepts (Michalski 1983).”
Machine Learning for Information Extraction in Informal Domains (2000, methodology): “5 (Quinlan, 1993), covering algorithms, such as AQ (Michalski, 1983) and CN2 (Clark & Boswell, 1991), and inductive logic programming (ILP) orelational learners, such as FOIL (Quinlan, 1990).”
Efficient search for association rules (2000, background): “Early approaches to identifying interesting rules from data were dominated by attempts to form small sets of rules for accurate classi cation of further previously unsighted data [9, 7, 13].”
Building Acceptable Classification Models (2010, background): “Michalski is one of the first to address the comprehensibility issue in Knowledge Discovery in Data [34].”
Learning Schemata for Natural Language Processing (1985, methodology): “This approach differs dramatically f rom most approaches to learning (e.g. [Michalski83, Mitchel l78, Winston70]) in which generalization is accomplished by extracting features which are shared by a number of examples.”