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The Use of a Bayesian Neural Network Model for Classification Tasks
, 1997
"... This thesis deals with a Bayesian neural network model. The focus is on how to use the model for automatic classification, i.e. on how to train the neural network to classify objects from some domain, given a database of labeled examples from the domain. The original Bayesian neural network is a one ..."
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Cited by 25 (1 self)
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This thesis deals with a Bayesian neural network model. The focus is on how to use the model for automatic classification, i.e. on how to train the neural network to classify objects from some domain, given a database of labeled examples from the domain. The original Bayesian neural network is a onelayer network implementing a naive Bayesian classifier. It is based on the assumption that different attributes of the objects appear independently of each other. This work has been aimed at extending the original Bayesian neural network model, mainly focusing on three different aspects. First the model is extended to a multilayer network, to relax the independence requirement. This is done by introducing a hidden layer of complex columns, groups of units which take input from the same set of input attributes. Two different types of complex column structures in the hidden layer are studied and compared. An information theoretic measure is used to decide which input attributes to consider toget...
Extensive Partition Operators, GrayLevel Connected Operators, and Region Merging/Classification Segmentation Algorithms: Theoretical Links
 IEEE Transactions on Image Processing
, 2001
"... The relation between morphological graylevel connected operators and segmentation algorithms based on region merging/classification strategies has been pointed out several times in the recent literature. However, to the best of our knowledge, the formal relation between them has not been establishe ..."
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Cited by 12 (0 self)
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The relation between morphological graylevel connected operators and segmentation algorithms based on region merging/classification strategies has been pointed out several times in the recent literature. However, to the best of our knowledge, the formal relation between them has not been established. This paper presents the link between the two domains based on the observation that both connected operators and segmentation algorithms share a key mechanism: they simultaneously operate on images and on partitions, and therefore they can be described as operations on a joint imagepartition model. As a result, we analyze both segmentation algorithms and connected operators by defining operators on complete product lattices, that explicitly model graylevel and partition attributes. In the first place, starting with a complete lattice of partitions, we initially define the concept of segmentation model as a mapping in a product lattice, whose elements are threetuples consisting of a partition, an image that models the partition attributes, and an image that represents the graylevel model associated to the segmentation. Then, assuming a conditional ordering relation, we show that any region merging/classification segmentation algorithm can be defined as an extensive operator in such a complete product lattice. In the second place, we proposed a very similar lattice based extended representation of graylevel functions in the context of connected operators, that highlights the mathematical analogy with segmentation algorithms, but in which the ordering relation is different. We use this framework to show that every region merging/classification segmentation algorithm indeed corresponds to a connected operator. While this result provides an explanation to previous work in ...
Semantic video object extraction using fourband watershed and partition lattice operators
 Proc. SPIE Vol. 4671 551 Downloaded From: http://proceedings.spiedigitallibrary.org/ on 04/28/2014 Terms of Use: http://spiedl.org/terms
, 2001
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Hierarchies Measuring Qualitative Variables
 In: Lecture Notes in Computer Science 2945 (SpringerVerlag 2004
, 2004
"... Abstract. Qualitative variables take symbolic values, such as hot, shoe, Europe or France. Sometimes, the values may be arranged in layers or levels of detail. For instance, the variable place_of_origin takes as level1 values European, African... as level2 values French, German... as level3 value ..."
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Cited by 7 (6 self)
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Abstract. Qualitative variables take symbolic values, such as hot, shoe, Europe or France. Sometimes, the values may be arranged in layers or levels of detail. For instance, the variable place_of_origin takes as level1 values European, African... as level2 values French, German... as level3 values Californian, Texan... The paper describes a hierarchy, a mathematical construct among these variables. The confusion resulting when using a value instead of another is defined, as well as the closeness to which object o fulfills predicate P. Other operations among and properties of hierarchical values are derived. Hierarchies are compared with ontologies. Hierarchies find use in measuring linguistic relatedness or similarity. Hierarchical variables abound and are commonly used, often with suggestive string values, without fully realizing or exploiting its properties. We deal with arbitrary hierarchies. Examples are given. 1
A.: Hierarchies as a New Data Type for Qualitative Variables
 Journal of Data Knowledge Engineering, Elsevier
, 2002
"... SUMMARY. Qualitative variables take symbolic values such as cat, orange, California, Africa. Often these values can be arranged in levels of deeper detail. For example, the variable place_of_birth takes as level1 values Africa, Asia... as level2 values Nigeria, Japan... as level3 values Californ ..."
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Cited by 6 (4 self)
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SUMMARY. Qualitative variables take symbolic values such as cat, orange, California, Africa. Often these values can be arranged in levels of deeper detail. For example, the variable place_of_birth takes as level1 values Africa, Asia... as level2 values Nigeria, Japan... as level3 values California, Massachusetts... These values are organized in a hierarchy H, a mathematical construct among these values. Over H, the following are defined: (1) the function confusion resulting when using a symbolic value instead of another; (2) the closeness to which object o fulfills predicate P; (3) a method which allows precisioncontrolled retrieval for relational databases whose objects have symbolic values.
COGNITIVE PROMPTINGS FOR SEMANTICMIND ANALYSIS AND OBJECTORIENTED DATA INTEGRATION OF INFORMATION FLOWS
 GEOPRO 2003
, 2003
"... An emerging area of digital data processing is the computerbased intelligent analysis of information flows. In this paper, we discuss some cognitive promptings that can lead to intelligent data (audio, images, video, mixed) analysis and synthesis. A companion paper in this book describes how to ext ..."
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Cited by 2 (2 self)
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An emerging area of digital data processing is the computerbased intelligent analysis of information flows. In this paper, we discuss some cognitive promptings that can lead to intelligent data (audio, images, video, mixed) analysis and synthesis. A companion paper in this book describes how to extract semantic components from unordered data sets (Gestalt problem) in visual information data (Analysis) and an application of our approach is illustrated with a rasterscanned color cartographic map interpretation system⎯AnalogicaltoRastertoVector (A2R2V).
Data Semantic Associative Analysis and Synthesis
"... An emerging area of digital data processing is the computerbased intelligent analysis and synthesis of information flows/streams. These include in particular processing of audio, hypertext, image, text, video, mixed, etc. data that travel over the Internet. The final goal of most current research e ..."
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An emerging area of digital data processing is the computerbased intelligent analysis and synthesis of information flows/streams. These include in particular processing of audio, hypertext, image, text, video, mixed, etc. data that travel over the Internet. The final goal of most current research efforts is to build the Semantic Web in the sense defined by the World Wide Web inventor Tim BernersLee. One of the main problems for success of this project is how to automatically extract (and then interpret) semantic components from unordered data flows/streams. This problem was already stated by Gestalt school in early 1920s in case of the human visual perception. Note that an efficient humanmachine interaction is the stumbling block of modern computer technologies. In this chapter, we are going to discuss the foundations of semantic associative data analysis and synthesis to approach the solution of these fundamental problems, whose solutions are indispensable for the Semantic Web development. Data semantic associative analysis and synthesis consists of two main components – semanticmind data analysis and objectoriented data integration (synthesis). They will be described in this chapter.
VK The Use of a Bayesian Neural Network Model for Classification Tasks
, 1997
"... This thesis deals with a Bayesian neural network model. The focus is on how to use the model for automatic classification, i.e. on how to train the neural network to classify objects from some domain, given a database of labeled examples from the domain. The original Bayesian neural network is a one ..."
Abstract
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This thesis deals with a Bayesian neural network model. The focus is on how to use the model for automatic classification, i.e. on how to train the neural network to classify objects from some domain, given a database of labeled examples from the domain. The original Bayesian neural network is a onelayer network implementing a naive Bayesian classifier. It is based on the assumption that different attributes of the objects appear independently of each other. This work has been aimed at extending the original Bayesian neural network model, mainly focusing on three different aspects. First the model is extended to a multilayer network, to relax the independence requirement. This is done by introducing a hidden layer of complex columns, groups of units which take input from the same set of input attributes. Two different types of complex column structures in the hidden layer are studied and compared. An information theoretic measure is used to decide which input attributes to consider together in complex columns. Also used are ideas from Bayesian statistics, as a means to estimate the probabilities from data which are required to set up the weights and biases in the neural network. The use of uncertain evidence and continuous valued attributes in the Bayesian neural network are also treated. Both things require the network to handle graded inputs, i. e. probability distributions over some discrete attributes given as input. Continuous valued attributes can then be handled by using
Image Models As Geographic Information Segmentation Sequences – IMAGISS
, 2006
"... Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the agencies. [Submitted to ISPRS] ..."
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Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the agencies. [Submitted to ISPRS]