scholarly journals Quantifying prior determination knowledge using the PAC learning model

1994 ◽  
Vol 17 (1) ◽  
pp. 69-105 ◽  
Author(s):  
Sridhar Mahadevan ◽  
Prasad Tadepalli
Keyword(s):  
Learning Model ◽  
Pac Learning ◽  
Pac Learning Model

Identifying Offline Muscle Strength Profiles Sufficient for Short-Duration Fes-Lce Exercise: A Pac Learning Model Approach

2006 ◽  
Vol 20 (3) ◽  
pp. 209-220 ◽  
Author(s):  
Randy D. Trumbower ◽  
Sanguthevar Rajasekaran ◽  
Pouran D. Faghri
Keyword(s):  
Muscle Strength ◽  
Short Duration ◽  
Learning Model ◽  
Pac Learning ◽  
Pac Learning Model ◽  
Model Approach

A Review of Some Extensions to the PAC Learning Model

1996 ◽  
pp. 43-64
Author(s):  
Sanjeev R. Kulkarni
Keyword(s):  
Learning Model ◽  
Pac Learning ◽  
Pac Learning Model

Relationships between learning and information compression based on PAC learning model

1993 ◽  
Vol 24 (8) ◽  
pp. 47-58 ◽  
Author(s):  
Eiji Takimoto ◽  
Akira Maruoka
Keyword(s):  
Learning Model ◽  
Pac Learning ◽  
Information Compression ◽  
Pac Learning Model

Investigating the Distribution Assumptions in the Pac Learning Model

COLT ◽  
1991 ◽  
pp. 24-32 ◽  
Author(s):  
Peter L. Bartlett ◽  
Robert C. Williamson
Keyword(s):  
Learning Model ◽  
Pac Learning ◽  
Pac Learning Model

scholarly journals Learning Query Inseparable εℒℋ Ontologies

2020 ◽  
Vol 34 (03) ◽  
pp. 2959-2966
Author(s):  
Ana Ozaki ◽  
Cosimo Persia ◽  
Andrea Mazzullo
Keyword(s):  
Learning Process ◽  
Query Language ◽  
Learning Model ◽  
Pac Learning ◽  
Exact Learning ◽  
Pac Learning Model

We investigate the complexity of learning query inseparable εℒℋ ontologies in a variant of Angluin's exact learning model. Given a fixed data instance A* and a query language 𝒬, we are interested in computing an ontology ℋ that entails the same queries as a target ontology 𝒯 on A*, that is, ℋ and 𝒯 are inseparable w.r.t. A* and 𝒬. The learner is allowed to pose two kinds of questions. The first is ‘Does (𝒯,A)⊨ q?’, with A an arbitrary data instance and q and query in 𝒬. An oracle replies this question with ‘yes’ or ‘no’. In the second, the learner asks ‘Are ℋ and 𝒯 inseparable w.r.t. A* and 𝒬?’. If so, the learning process finishes, otherwise, the learner receives (A*,q) with q ∈ 𝒬, (𝒯,A*) |= q and (ℋ,A*) ⊭ q (or vice-versa). Then, we analyse conditions in which query inseparability is preserved if A* changes. Finally, we consider the PAC learning model and a setting where the algorithms learn from a batch of classified data, limiting interactions with the oracles.

Towards an Adaptive Educational Course on the Mathematical Foundations of Machine Learning

2021 ◽  
Vol 12 (8) ◽  
pp. 431-439
Author(s):  
A. S. Shundeev ◽  
Keyword(s):  
Machine Learning ◽  
Binary Classification ◽  
Classification Problem ◽  
Learning Model ◽  
Pac Learning ◽  
Learning Methods ◽  
Variable Part ◽  
Machine Learning Methods ◽  
Mathematical Foundations ◽  
Educational Course

Today the development of information technology is closely related to the creation and application of machine learning and data analysis methods. In this regard, the need for training specialists in this area is growing. Very often, the study of machine learning methods is combined with the study of a certain programming language and the tools of its specialized library. This approach is undoubtedly justified, because it provides the possibility of accelerated application of the knowledge gained in practice. At the same time, it should be noted that with this approach, it is rather not machine learning methods that are studied, but a certain set of methodological techniques for using the tools of the specialized library. The presented work is devoted to the experience of creating an adaptive educational course on the mathematical foundations of machine learning. This course is aimed at undergraduate and graduate students of mathematical specialties. It is divided into core and variable parts. The obligatory core part is built around the PAC learning model and the binary classification problem. Within the variable part, issues of the weak learning model and the boosting methods are considered. Also a methodology of changing the variable part of the course is discussed.

A hybrid learning model of tactical decision making

2000 ◽  
Author(s):  
Todd R. Johnson ◽  
Jiajie Zhang
Keyword(s):  
Decision Making ◽  
Hybrid Learning ◽  
Learning Model ◽  
Tactical Decision
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