The optimal PAC bound for intersection-closed concept classes

2015 ◽  
Vol 115 (4) ◽  
pp. 458-461
Author(s):  
Malte Darnstädt
Keyword(s):  
Concept Classes

scholarly journals Structured (De)composable Representations Trained with Neural Networks

Computers ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 79
Author(s):  
Graham Spinks ◽  
Marie-Francine Moens
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Contextual Information ◽  
Learning To Learn ◽  
Class Label ◽  
Impact Performance ◽  
Novel Technique ◽  
Language Data ◽  
Concept Classes ◽  
Generic Representation

This paper proposes a novel technique for representing templates and instances of concept classes. A template representation refers to the generic representation that captures the characteristics of an entire class. The proposed technique uses end-to-end deep learning to learn structured and composable representations from input images and discrete labels. The obtained representations are based on distance estimates between the distributions given by the class label and those given by contextual information, which are modeled as environments. We prove that the representations have a clear structure allowing decomposing the representation into factors that represent classes and environments. We evaluate our novel technique on classification and retrieval tasks involving different modalities (visual and language data). In various experiments, we show how the representations can be compressed and how different hyperparameters impact performance.

scholarly journals Bounding the Vapnik-Chervonenkis dimension of concept classes parameterized by real numbers

1995 ◽  
Vol 18 (2-3) ◽  
pp. 131-148 ◽  
Author(s):  
Paul W. Goldberg ◽  
Mark R. Jerrum
Keyword(s):  
Real Numbers ◽  
Concept Classes

scholarly journals Learning nested differences of intersection-closed concept classes

1990 ◽  
Vol 5 (2) ◽  
pp. 165-196 ◽  
Author(s):  
David Helmbold ◽  
Robert Sloan ◽  
Manfred K. Warmuth
Keyword(s):  
Concept Classes

k-Fold unions of low-dimensional concept classes

2009 ◽  
Vol 109 (23-24) ◽  
pp. 1232-1234 ◽  
Author(s):  
David Eisenstat
Keyword(s):  
Low Dimensional ◽  
Concept Classes

Developing a Dietary Lifestyle Ontology (DILON) to Improve the Interoperability of Dietary Data (Preprint)

2021 ◽  
Author(s):  
Hyeoneui Kim ◽  
Jinsun Jung ◽  
Jisung Choi
Keyword(s):  
Dietary Habits ◽  
Dietary Assessment ◽  
Dietary Habit ◽  
Assessment Data ◽  
Common Data Elements ◽  
Metadata Annotation ◽  
Data Elements ◽  
Concept Classes ◽  
Colloquial Style ◽  
Dietary Data

BACKGROUND Dietary habits offer crucial information on one's health and form a considerable part of the Patient-Generated Health Data (PGHD). Dietary data are collected through various channels and formats; thus, interoperability is a significant challenge to reusing the data. The vast scope of dietary concepts and colloquial style of expression add difficulty to the standardization task. Common Data Elements (CDE) with metadata annotation and ontological structuring of dietary concepts address the interoperability issues of dietary data to some extent. However, challenges remaining in making culture-specific dietary habits and questionnaire-based dietary assessment data interoperable require additional efforts. OBJECTIVE The main goal of this study was to address the interoperability challenge in dietary concepts by combining ontological curation of dietary concepts and metadata annotation of questionnaire-based dietary data. Specifically, this study aimed to develop a Dietary Lifestyle Ontology (DILON) and demonstrated the improved interoperability of questionnaire-based dietary data by annotating its main semantics with DILON. METHODS By analyzing 1158 dietary assessment data elements (367 in Korean and 791 in English), 515 dietary concepts were extracted and used to construct DILON. To demonstrate the utility of DILON in improving the interoperability of multi-cultural questionnaire-based dietary data, ten Competency Questions (CQs) were developed that identified data elements that share the same dietary topics and measurement qualities. As the test cases, 68 dietary habit data elements from Korean and English questionnaires were instantiated and annotated with the dietary concepts in DILON. The competency questions were translated into Semantic Query-enhanced Web Rule Language (SQWRL), and the query results were reviewed for accuracy. RESULTS DILON was built with 260 concept classes and 486 instances and successfully validated with ontology validation tools. A small overlap (72 concepts) in the concepts extracted from the questionnaires in two languages indicates the need to pay closer attention to representing culture-specific dietary concepts. The SQWRL queries reflecting the 10 CQs yielded the correct results. CONCLUSIONS Ensuring the interoperability of dietary lifestyle data is a demanding task due to its vast scope and variations in expression. This study demonstrated that, when combined with common data elements and semantic metadata annotation, ontology can effectively mediate the interoperability of dietary data generated in different cultural contexts and expressed in various styles.

scholarly journals PAC learning of concept classes through the boundaries of their items

1997 ◽  
Vol 172 (1-2) ◽  
pp. 91-120 ◽  
Author(s):  
B. Apolloni ◽  
S. Chiaravalli
Keyword(s):  
Pac Learning ◽  
Concept Classes

scholarly journals Constraining classifiers in molecular analysis: invariance and robustness

2020 ◽  
Vol 17 (163) ◽  
pp. 20190612
Author(s):  
Ludwig Lausser ◽  
Robin Szekely ◽  
Attila Klimmek ◽  
Florian Schmid ◽  
Hans A. Kestler
Keyword(s):  
Ad Hoc ◽  
A Priori ◽  
Robustness Analysis ◽  
Molecular Classification ◽  
Support Vector ◽  
Complexity Classes ◽  
Invariance Properties ◽  
Standard Linear ◽  
Concept Classes ◽  
Microarray Datasets

Analysing molecular profiles requires the selection of classification models that can cope with the high dimensionality and variability of these data. Also, improper reference point choice and scaling pose additional challenges. Often model selection is somewhat guided by ad hoc simulations rather than by sophisticated considerations on the properties of a categorization model. Here, we derive and report four linked linear concept classes/models with distinct invariance properties for high-dimensional molecular classification. We can further show that these concept classes also form a half-order of complexity classes in terms of Vapnik–Chervonenkis dimensions, which also implies increased generalization abilities. We implemented support vector machines with these properties. Surprisingly, we were able to attain comparable or even superior generalization abilities to the standard linear one on the 27 investigated RNA-Seq and microarray datasets. Our results indicate that a priori chosen invariant models can replace ad hoc robustness analysis by interpretable and theoretically guaranteed properties in molecular categorization.

scholarly journals Pseudo-dimension of quantum circuits

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Matthias C. Caro ◽  
Ishaun Datta
Keyword(s):  
Probability Distributions ◽  
Expressive Power ◽  
Upper Bounds ◽  
Quantum Circuits ◽  
Polynomial Size ◽  
Probabilistic Concept ◽  
Circuit Depth ◽  
Concept Classes ◽  
Circuit Output ◽  
Output Probability

AbstractWe characterize the expressive power of quantum circuits with the pseudo-dimension, a measure of complexity for probabilistic concept classes. We prove pseudo-dimension bounds on the output probability distributions of quantum circuits; the upper bounds are polynomial in circuit depth and number of gates. Using these bounds, we exhibit a class of circuit output states out of which at least one has exponential gate complexity of state preparation, and moreover demonstrate that quantum circuits of known polynomial size and depth are PAC-learnable.

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