Non-parametric Statistical Learning Methods for Inductive Classifiers in Semantic Knowledge Bases

2008 ◽  
Author(s):  
Claudia d'Amato ◽  
Nicola Fanizzi ◽  
Floriana Esposito
Keyword(s):  
Statistical Learning ◽  
Knowledge Bases ◽  
Semantic Knowledge ◽  
Learning Methods ◽  
Non Parametric

INDUCTION OF CLASSIFIERS THROUGH NON-PARAMETRIC METHODS FOR APPROXIMATE CLASSIFICATION AND RETRIEVAL WITH ONTOLOGIES

2008 ◽  
Vol 02 (03) ◽  
pp. 403-423 ◽  
Author(s):  
NICOLA FANIZZI ◽  
CLAUDIA D'AMATO ◽  
FLORIANA ESPOSITO
Keyword(s):  
Semantic Web ◽  
Statistical Learning ◽  
Kernel Function ◽  
Nearest Neighbor ◽  
Knowledge Bases ◽  
Support Vector ◽  
Parametric Methods ◽  
K Nearest Neighbor ◽  
K Nearest Neighbor Algorithm ◽  
Non Parametric

This work concerns non-parametric approaches for statistical learning applied to the standard knowledge representation languages adopted in the Semantic Web context. We present methods based on epistemic inference that are able to elicit and exploit the semantic similarity of individuals in OWL knowledge bases. Specifically, a totally semantic and language-independent semi-distance function is introduced, whence also an epistemic kernel function for Semantic Web representations is derived. Both the measure and the kernel function are embedded in non-parametric statistical learning algorithms customized for coping with Semantic Web representations. Particularly, the measure is embedded in a k-Nearest Neighbor algorithm and the kernel function is embedded in a Support Vector Machine. The implemented algorithms are used to perform inductive concept retrieval and query answering. An experimentation on real ontologies proves that the methods can be effectively employed for performing the target tasks, and moreover that it is possible to induce new assertions that are not logically derivable.

scholarly journals Review of Statistical Learning Methods in Integrated Omics Studies (An Integrated Information Science)

2018 ◽  
Vol 12 ◽  
pp. 117793221875929 ◽  
Author(s):  
Irene Sui Lan Zeng ◽  
Thomas Lumley
Keyword(s):  
Machine Learning ◽  
Statistical Learning ◽  
Information Science ◽  
Molecular System ◽  
Principal Component ◽  
Statistical Machine Learning ◽  
Learning Methods ◽  
Integrated Omics ◽  
Types Of Information ◽  
Integrated Information

Integrated omics is becoming a new channel for investigating the complex molecular system in modern biological science and sets a foundation for systematic learning for precision medicine. The statistical/machine learning methods that have emerged in the past decade for integrated omics are not only innovative but also multidisciplinary with integrated knowledge in biology, medicine, statistics, machine learning, and artificial intelligence. Here, we review the nontrivial classes of learning methods from the statistical aspects and streamline these learning methods within the statistical learning framework. The intriguing findings from the review are that the methods used are generalizable to other disciplines with complex systematic structure, and the integrated omics is part of an integrated information science which has collated and integrated different types of information for inferences and decision making. We review the statistical learning methods of exploratory and supervised learning from 42 publications. We also discuss the strengths and limitations of the extended principal component analysis, cluster analysis, network analysis, and regression methods. Statistical techniques such as penalization for sparsity induction when there are fewer observations than the number of features and using Bayesian approach when there are prior knowledge to be integrated are also included in the commentary. For the completeness of the review, a table of currently available software and packages from 23 publications for omics are summarized in the appendix.

Effective Statistical Learning Methods for Actuaries I

2019 ◽  
Author(s):  
Michel Denuit ◽  
Donatien Hainaut ◽  
Julien Trufin
Keyword(s):  
Statistical Learning ◽  
Learning Methods
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