Xenia Alexandre Naidenova

Xenia Alexandre NaidenovaNaidenova Xenia was born at Leningrad (Saint-Petersburg, the Russian Federation) in 1940. She graduated from Lenin Electro-Technical Institute of Leningrad (now Saint-Petersburg Electro-Technical University) in 1963 and received the Diploma on computer engineering. From this institute, she received her doctor’s degree (Ph.D.) in Technical Sciences in 1979. She had been an invited professor at the University of Paris-Sud, ORSAY, FRANCE, Research Laboratory of Information Sciences under the head of Dr. N. Spyratos, March, 1991. In 1995, she started to work as senior researcher at the Research Centre of Saint-Petersburg Military Medical Academy where she is engaged in developing knowledge discovery and data mining program systems to support solving medicine and psychological diagnostic tasks. Under Xenia Naidenova, some advanced knowledge acquisition systems based on machine learning original algorithms have been developed including a tool for adaptive programming applied diagnostic medical systems. She received the Diploma of Senior Researcher from the Military Medical Academy in 1999. In 2010, she received the Award of the Russian Association for Artificial Intelligence for the best fundamental work in Artificial Intelligence. She has published over 200 papers on a wide range of topics in computer science; she is also the editor (together with Ignatov, D.) of the monograph “Diagnostic Test Approaches to Machine Learning and Commonsense Reasoning Systems” (IGI Global, 2013). She is a Fellow of the Russian Association for Artificial Intelligence founded in 1989. She works as a constant member of the Organizing Committee of the International Conference “Knowledge-Dialog-Solution”. In 2011, she has been a member of the Program Committee of the Workshop “Soft Computing Applications and Knowledge Discovery".

Publications

Incremental Approach to Classification Learning
Incremental Approach to Classification Learning
Xenia Alexandre Naidenova. © 2019. 13 pages.
An approach to incremental classification learning is proposed. Classification learning is based on approximation of a given partitioning of objects into disjointed blocks in...
Incremental Approach to Classification Learning
Incremental Approach to Classification Learning
Xenia Alexandre Naidenova. © 2018. 11 pages.
An approach to incremental classification learning is proposed. Classification learning is based on approximation of a given partitioning of objects into disjoint blocks in...
Adding Context into Classification Reasoning Based on Good Classification Tests
Adding Context into Classification Reasoning Based on Good Classification Tests
Xenia Naidenova. © 2016. 21 pages.
In this chapter, classification reasoning is considered. The concept of good classification test lies in the foundation of this reasoning. Inferring good classification tests...
Classification Reasoning as a Basic Part of Machine Learning
Classification Reasoning as a Basic Part of Machine Learning
Xenia Naidenova. © 2015. 8 pages.
Diagnostic Test Approaches to Machine Learning and Commonsense Reasoning Systems
Diagnostic Test Approaches to Machine Learning and Commonsense Reasoning Systems
Xenia Naidenova, Dmitry I. Ignatov. © 2013. 367 pages.
The consideration of symbolic machine learning algorithms as an entire class will make it possible, in the future, to generate algorithms, with the aid of some parameters...
Constructing Galois Lattices as a Commonsense Reasoning Process
Constructing Galois Lattices as a Commonsense Reasoning Process
Xenia Naidenova. © 2013. 37 pages.
The concept of good classification test is used in this chapter as a dual element of the interconnected algebraic lattices. The operations of lattice generation take their...
An Analytical Survey of Current Approaches to Mining Logical Rules from Data
An Analytical Survey of Current Approaches to Mining Logical Rules from Data
Xenia Naidenova. © 2013. 31 pages.
An analytical survey of some efficient current approaches to mining all kind of logical rules is presented including implicative and functional dependencies, association and...
Machine Learning as a Commonsense Reasoning Process
Machine Learning as a Commonsense Reasoning Process
Xenia Naidenova. © 2012. 7 pages.
One of the most important tasks in database technology is to combine the following activities: data mining or inferring knowledge from data and query processing or reasoning on...
Integration of the Image and NL-text Analysis/Synthesis Systems
Integration of the Image and NL-text Analysis/Synthesis Systems
Gennady K. Khakhalin, Sergey S. Kurbatov, Xenia Naidenova, Alex P. Lobzin. © 2012. 26 pages.
A complex combining multimodal intelligent systems is described. The complex consists of the following systems: image analyzer, image synthesizer, linguistic analyzer of NL-text...
Machine Learning Methods for Commonsense Reasoning Processes: Interactive Models
Machine Learning Methods for Commonsense Reasoning Processes: Interactive Models
Xenia Naidenova. © 2010. 424 pages.
The reduction of machine learning algorithms to commonsense reasoning processes is now possible due to the reformulation of machine learning problems as searching the best...
Knowledge in the Psychology of Thinking and Mathematics
Knowledge in the Psychology of Thinking and Mathematics
Xenia Naidenova. © 2010. 33 pages.
This chapter offers a view on the history of developing the concepts of knowledge and human reasoning both in mathematics and psychology. Mathematicians create the formal...
Logic-Based Reasoning in the Framework of Artificial Intelligence
Logic-Based Reasoning in the Framework of Artificial Intelligence
Xenia Naidenova. © 2010. 42 pages.
This chapter focuses on the tasks of knowledge engineering related mainly to knowledge acquisition and modeling integrated logic-based inference. We have overlooked the principal...
The Coordination of Commonsense Reasoning Operations
The Coordination of Commonsense Reasoning Operations
Xenia Naidenova. © 2010. 12 pages.
In this chapter, a conception of commonsense reasoning is developed based on mutually coordinated operations on objects, classes of objects, and properties of objects. This...
The Logical Rules of Commonsense Reasoning
The Logical Rules of Commonsense Reasoning
Xenia Naidenova. © 2010. 14 pages.
In this chapter we describe a model of commonsense reasoning that has been acquired from our numerous investigations on the human reasoning modes used by experts for solving...
The Examples of Human Commonsense Reasoning Processes
The Examples of Human Commonsense Reasoning Processes
Xenia Naidenova. © 2010. 20 pages.
In this chapter, we concentrate our attention on analyzing and modeling natural human reasoning in solving different tasks: pattern recognition in scientific investigations...
Machine Learning (ML) as a Diagnostic Task
Machine Learning (ML) as a Diagnostic Task
Xenia Naidenova. © 2010. 43 pages.
This chapter discusses a revised definition of classification (diagnostic) test. This definition allows considering the problem of inferring classification tests as the task of...
The Concept of Good Classification (Diagnostic) Test
The Concept of Good Classification (Diagnostic) Test
Xenia Naidenova. © 2010. 46 pages.
In this chapter, the definition of good diagnostic test and the characterization of good tests are introduced and the concepts of good maximally redundant and good irredundant...
The Duality of Good Diagnostic Tests
The Duality of Good Diagnostic Tests
Xenia Naidenova. © 2010. 34 pages.
The concept of good classification test is redefined in this chapter as a dual element of interconnected algebraic lattices. The operations of lattice generation take their...
Towards an Integrative Model of Deductive-Inductive Commonsense Reasoning
Towards an Integrative Model of Deductive-Inductive Commonsense Reasoning
Xenia Naidenova. © 2010. 34 pages.
The most important steps in the direction to an integrative model of deductive-inductive commonsense reasoning are made in this chapter. The decomposition of inferring good...
Towards a Model of Fuzzy Commonsense Reasoning
Towards a Model of Fuzzy Commonsense Reasoning
Xenia Naidenova. © 2010. 40 pages.
This chapter summarizes some methods of inferring approximate diagnostic tests. Considering the sets of approximately minimal diagnostic tests as “characteristic portraits” of...
Object-Oriented Technology for Expert System Generation
Object-Oriented Technology for Expert System Generation
Xenia Naidenova. © 2010. 18 pages.
A technology for rapid prototyping expert systems or intelligent systems as a whole is proposed. The main constituents of the technology are the object-oriented model of data and...
Case Technology for Psycho-Diagnostic System Generation
Case Technology for Psycho-Diagnostic System Generation
Xenia Naidenova. © 2010. 29 pages.
The automated workstation (AWS) for psychologists and physiologists must be an instrument that allows adaptive programming applied psycho-diagnostic expert systems (APDS). For...
Commonsense Reasoning in Intelligent Computer Systems
Commonsense Reasoning in Intelligent Computer Systems
Xenia Naidenova. © 2010. 36 pages.
This chapter deals with the description of possible mechanisms for data-knowledge organization and management in intelligent computer systems. Challenges and future trends will...
Interconnecting a Class of Machine Learning Algorithms with Logical Commonsense Reasoning Operations
Interconnecting a Class of Machine Learning Algorithms with Logical Commonsense Reasoning Operations
Xenia Naidenova. © 2010. 33 pages.
The purpose of this chapter is to demonstrate the possibility of transforming a large class of machine learning algorithms into commonsense reasoning processes based on using...
Machine Learning as a Commonsense Reasoning Process
Machine Learning as a Commonsense Reasoning Process
Xenia Naidenova. © 2009. 7 pages.
One of the most important tasks in database technology is to combine the following activities: data mining or inferring knowledge from data and query processing or reasoning on...
Reducing a Class of Machine Learning Algorithms to Logical Commonsense Reasoning Operations
Reducing a Class of Machine Learning Algorithms to Logical Commonsense Reasoning Operations
Xenia Naidenova. © 2008. 24 pages.
The purpose of this paper is to demonstrate the possibility of transforming a large class of machine learning algorithms into commonsense reasoning processes based on using...