Exact learning for infinite families of concepts
@article{Moshkov2022ExactLF, title={Exact learning for infinite families of concepts}, author={Mikhail Ju. Moshkov}, journal={ArXiv}, year={2022}, volume={abs/2201.08225} }
In this paper, based on results of exact learning, test theory, and rough set theory, we study arbitrary infinite families of concepts each of which consists of an infinite set of elements and an infinite set of subsets of this set called concepts. We consider the notion of a problem over a family of concepts that is described by a finite number of elements: for a given concept, we should recognize which of the elements under consideration belong to this concept. As algorithms for problem…
References
SHOWING 1-10 OF 27 REFERENCES
On the Depth of Decision Trees with Hypotheses
- Computer ScienceEntropy
- 2022
This paper investigates decision trees over infinite sets of binary attributes represented as infinite binary information systems and defines the notion of a problem over an information system and study three functions of the Shannon type, which characterize the dependence in the worst case of the minimum depth of a decision tree solving a problem on the number of attributes in the problem description.
Lower Bound Methods and Separation Results for On-Line Learning Models
- Computer ScienceMachine Learning
- 2004
A model for learning with equivalence and membership queries is considered and lower bounds on the number of queries that are needed to learn a concept class are given.
Learning Quickly When Irrelevant Attributes Abound: A New Linear-Threshold Algorithm
- Computer Science28th Annual Symposium on Foundations of Computer Science (sfcs 1987)
- 1987
This work presents one such algorithm that learns disjunctive Boolean functions, along with variants for learning other classes of Boolean functions.
Queries and concept learning
- Computer ScienceMachine Learning
- 1988
We consider the problem of using queries to learn an unknown concept. Several types of queries are described and studied: membership, equivalence, subset, superset, disjointness, and exhaustiveness…
Time Complexity of Decision Trees
- Computer ScienceTrans. Rough Sets
- 2005
A collection of mathematical results on decision trees in areas of rough set theory and decision tree theory applications such as discrete optimization, analysis of acyclic programs, pattern recognition, fault diagnosis and probabilistic reasoning are contained.
How many queries are needed to learn?
- Computer Science, MathematicsJACM
- 1996
It is shown that an honest class is exactly polynomial-query learnable if and only if it is learnable using an oracle for Γp4, and a new relationship between query complexity and time complexity in exact learning is shown.
Minimizing Depth of Decision Trees with Hypotheses
- Computer ScienceIJCRS
- 2021
This paper presents dynamic programming algorithms for minimization of the depth of above decision trees and discusses results of computer experiments on various data sets and randomly generated Boolean functions.
Sorting by Decision Trees with Hypotheses (extended abstract)
- Computer ScienceCS&P
- 2021
Decision trees that use both queries based on one attribute each and queriesbased on hypotheses about values of all attributes are considered, similar to ones studied in exact learning.