Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
A vector-valued measure as a basis of a Banach space.
Date
1998-02-01
Author
Zheltukhın, Kostyantyn
Metadata
Show full item record
Item Usage Stats
70
views
0
downloads
Cite This
A new notion of the basic measure is introdused. The vector-valued measures which are basic measures are described. It is proved that the Banach space has a basic measure iff it is isomorphic to the order continuous Banach lattice with a weak unit. The connection between the properties of the Banach space and those of the basic measure is investigated.
URI
https://hdl.handle.net/11511/84832
http://jmage.ilt.kharkov.ua/
Journal
Journal of Mathematical Physics, Analysis, Geometry
Collections
Department of Mathematics, Article
Suggestions
OpenMETU
Core
A statistical unified framework for rank-based multiple classifier decision combination
Saranlı, Afşar (2001-04-01)
This study presents a theoretical investigation of the rank-based multiple classifier decision combination problem, with the aim of providing a unified framework to understand a variety of such systems. The combination of the decisions of more than one classifiers with the aim of improving overall system performance is a concept of general interest in pattern recognition, as a viable alternative to designing a single sophisticated classifier. The problem of combining the classifier decisions in the raw form...
Automated coherence detection with term-distance path extraction of the co-occurrence matrix of a document
Ağın, Halil; Acartürk, Cengiz; Department of Cognitive Sciences (2015)
This thesis takes the distributional semantics (frequency-based semantics) approach as the theoretical framework to quantify textual coherence. Distributional semantics describes discourse sections as vectors, having dimensions are the frequency count of co-occurring words in the text within its semantic space. It quantifies the textual coherence by measuring the cosine values of vectors of successive sentences (cf. Latent Semantic Analysis, LSA). The common assumption underlying LSA based studies is that t...
A Model Selection criterion for the Mixture Reduction problem based on the Kullback-Leibler Divergence
D'Ortenzio, Alessandro; Manes, Costanzo; Orguner, Umut (2022-01-01)
In order to be properly addressed, many practical problems require an accurate stochastic characterization of the involved uncertainties. In this regard, a common approach is the use of mixtures of parametric densities which allow, in general, to arbitrarily approximate complex distributions by a sum of simpler elements. Nonetheless, in contexts like target tracking in clutter, where mixtures of densities are commonly used to approximate the posterior distribution, the optimal Bayesian recursion leads to a ...
A Solution to the Paradox of Idealization in Modal Epistemic Languages
Akçelik, Oğuz (2016-02-01)
Human beings are endowed with finite cognitive capacities so that there are forever unknowntruths. This fact is stated by non-omniscience thesis (NO). On the other hand many philosophers, especially semantic anti-realists, hold that all truths (even the unknown ones)are knowable, and this is stated by the knowability principle (KP). The so-called Paradox of Idealization consists in the derivation of a contradiction from the following, initially plausible, premises. First, thesis (FU) stating that there ...
Deep metric learning with distance sensitive entangled triplet losses
Karaman, Kaan; Alatan, Abdullah Aydın; Department of Electrical and Electronics Engineering (2021-2-12)
Metric learning aims to define a distance that is able to measure the semantic difference between the instances in a dataset. The most recent approaches in this area mostly utilize deep neural networks as their models to map the input data into a feature space by finding appropriate distance metrics between the features. A number of loss functions are already defined in the literature based on these similarity metrics to discriminate instances in the feature space. In this thesis, we particularly focus on t...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
K. Zheltukhın, “A vector-valued measure as a basis of a Banach space.,”
Journal of Mathematical Physics, Analysis, Geometry
, pp. 25–34, 1998, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/84832.