The Classification Problem for Amenable C*-Algebras

1995 ◽  
pp. 922-932 ◽  
Author(s):  
George A. Elliott
Keyword(s):  
Classification Problem ◽  
C Algebras

scholarly journals THE CLASSIFICATION PROBLEM FOR AUTOMORPHISMS OF C*-ALGEBRAS

2015 ◽  
Vol 21 (4) ◽  
pp. 402-424 ◽  
Author(s):  
MARTINO LUPINI
Keyword(s):  
Complexity Theory ◽  
Classification Problem ◽  
C Algebras ◽  
Borel Complexity ◽  
Recent Developments

AbstractWe present an overview of the recent developments in the study of the classification problem for automorphisms of C*-algebras from the perspective of Borel complexity theory.

scholarly journals On the classification problem for nuclear C∗-algebras

2008 ◽  
Vol 167 (3) ◽  
pp. 1029-1044 ◽  
Author(s):  
Andrew Toms
Keyword(s):  
Classification Problem ◽  
C Algebras

scholarly journals Geometric Classification of Graph C*-algebras over Finite Graphs

2018 ◽  
Vol 70 (2) ◽  
pp. 294-353 ◽  
Author(s):  
Søren Eilers ◽  
Gunnar Restorff ◽  
Efren Ruiz ◽  
Adam P.W. Sørensen
Keyword(s):  
Standard Condition ◽  
Classification Problem ◽  
Complete Classification ◽  
Type I ◽  
Simple Graphs ◽  
Finite Graphs ◽  
C Algebras ◽  
Special Case

AbstractWe address the classification problem for graph C*-algebras of finite graphs (finitely many edges and vertices), containing the class of Cuntz-Krieger algebras as a prominent special case. Contrasting earlier work, we do not assume that the graphs satisfy the standard condition (K), so that the graph C*-algebras may come with uncountably many ideals.We find that in this generality, stable isomorphism of graph C*-algebras does not coincide with the geometric notion of Cuntz move equivalence. However, adding a modest condition on the graphs, the two notions are proved to be mutually equivalent and equivalent to the C*-algebras having isomorphicK-theories. This proves in turn that under this condition, the graph C*-algebras are in fact classifiable byK-theory, providing, in particular, complete classification when the C* - algebras in question are either of real rank zero or type I/postliminal. The key ingredient in obtaining these results is a characterization of Cuntz move equivalence using the adjacency matrices of the graphs.Our results are applied to discuss the classification problem for the quantumlens spaces defined by Hong and Szymański, and to complete the classification of graph C*-algebras associated with all simple graphs with four vertices or less.

An Introduction to K-Theory for C*-Algebras

2000 ◽  
Author(s):  
M. Rørdam ◽  
F. Larsen ◽  
N. Laustsen
Keyword(s):  
C Algebras ◽  
K Theory

Prognostication Stereotype of Patients Morbidity and Mortality by Extraction of E-Health Records

2018 ◽  
Vol 6 (6) ◽  
pp. 215
Author(s):  
Sunitha .T ◽  
Shyamala .J ◽  
Annie Jesus Suganthi Rani.A
Keyword(s):  
At Risk ◽  
Risk Prediction ◽  
Health Risks ◽  
Preventive Intervention ◽  
Classification Problem ◽  
Health Examination ◽  
Disease Area ◽  
Health Records ◽  
Mortality And Morbidity ◽  
Main Motive

Data mining suggest an innovative way of prognostication stereotype of Patients health risks. Large amount of Electronic Health Records (EHRs) collected over the years have provided a rich base for risk analysis and prediction. An EHR contains digitally stored healthcare information about an individual, such as observations, laboratory tests, diagnostic reports, medications, procedures, patient identifying information and allergies. A special type of EHR is the Health Examination Records (HER) from annual general health check-ups. Identifying participants at risk based on their current and past HERs is important for early warning and preventive intervention. By “risk”, we mean unwanted outcomes such as mortality and morbidity. This approach is limited due to the classification problem and consequently it is not informative about the specific disease area in which a personal is at risk. Limited amount of data extracted from the health record is not feasible for providing the accurate risk prediction. The main motive of this project is for risk prediction to classify progressively developing situation with the majority of the data unlabeled.

Solving the Message Classification Problem in Voice Interaction Systems

Vestnik MEI ◽  
2020 ◽  
Vol 5 (5) ◽  
pp. 132-139
Author(s):  
Ivan E. Kurilenko ◽  
◽  
Igor E. Nikonov ◽  
Keyword(s):  
Artificial Intelligence ◽  
Classification Problem ◽  
Subject Area ◽  
Text Messages ◽  
Case Based Reasoning ◽  
Short Text ◽  
Proposed Modification ◽  
Voice Interaction ◽  
Text Content ◽  
Case Based

A method for solving the problem of classifying short-text messages in the form of sentences of customers uttered in talking via the telephone line of organizations is considered. To solve this problem, a classifier was developed, which is based on using a combination of two methods: a description of the subject area in the form of a hierarchy of entities and plausible reasoning based on the case-based reasoning approach, which is actively used in artificial intelligence systems. In solving various problems of artificial intelligence-based analysis of data, these methods have shown a high degree of efficiency, scalability, and independence from data structure. As part of using the case-based reasoning approach in the classifier, it is proposed to modify the TF-IDF (Term Frequency - Inverse Document Frequency) measure of assessing the text content taking into account known information about the distribution of documents by topics. The proposed modification makes it possible to improve the classification quality in comparison with classical measures, since it takes into account the information about the distribution of words not only in a separate document or topic, but in the entire database of cases. Experimental results are presented that confirm the effectiveness of the proposed metric and the developed classifier as applied to classification of customer sentences and providing them with the necessary information depending on the classification result. The developed text classification service prototype is used as part of the voice interaction module with the user in the objective of robotizing the telephone call routing system and making a shift from interaction between the user and system by means of buttons to their interaction through voice.

scholarly journals Machine Learning for the Classification of Alzheimer’s Disease and Its Prodromal Stage Using Brain Diffusion Tensor Imaging Data: A Systematic Review

Processes ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1071
Author(s):  
Lucia Billeci ◽  
Asia Badolato ◽  
Lorenzo Bachi ◽  
Alessandro Tonacci
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Diffusion Tensor Imaging ◽  
Magnetic Resonance ◽  
Diffusion Tensor ◽  
Classification Problem ◽  
Computer Algorithms ◽  
Imaging Data

Alzheimer’s disease is notoriously the most common cause of dementia in the elderly, affecting an increasing number of people. Although widespread, its causes and progression modalities are complex and still not fully understood. Through neuroimaging techniques, such as diffusion Magnetic Resonance (MR), more sophisticated and specific studies of the disease can be performed, offering a valuable tool for both its diagnosis and early detection. However, processing large quantities of medical images is not an easy task, and researchers have turned their attention towards machine learning, a set of computer algorithms that automatically adapt their output towards the intended goal. In this paper, a systematic review of recent machine learning applications on diffusion tensor imaging studies of Alzheimer’s disease is presented, highlighting the fundamental aspects of each work and reporting their performance score. A few examined studies also include mild cognitive impairment in the classification problem, while others combine diffusion data with other sources, like structural magnetic resonance imaging (MRI) (multimodal analysis). The findings of the retrieved works suggest a promising role for machine learning in evaluating effective classification features, like fractional anisotropy, and in possibly performing on different image modalities with higher accuracy.

Sign in / Sign up

Export Citation Format

Share Document