KES-2012-Pattern-Recognition-Letters-issue-scope - Historial de revisiones

Manuel.grana en 13:06 7 ene 2013

2013-01-07T13:06:05Z

← Revisión anterior		Revisión del 15:06 7 ene 2013
Línea 12:		Línea 12:
	content based image retrieval (CBIR)		content based image retrieval (CBIR)
	:- Combining multi-modal information which can not be subject of conventional error measures, impeding the application of state-of-the-art error minimization approaches. The sources of information are heterogeneous, and its combination leads to unmeasurable data types. For instance, in the field of biomedical information processing, a wide diversity information sources may be used to reach a diagnosis that can be validated by the human operator.		:- Combining multi-modal information which can not be subject of conventional error measures, impeding the application of state-of-the-art error minimization approaches. The sources of information are heterogeneous, and its combination leads to unmeasurable data types. For instance, in the field of biomedical information processing, a wide diversity information sources may be used to reach a diagnosis that can be validated by the human operator.
	:- New views on uncertainty, ambiguity and data artifacts (i.e. noise, missing data)		:- New views on uncertainty, ambiguity and data artifacts (i.e. noise, missing data) allowing to effectively deal with them in a systematic and integrative way.
	allowing to effectively deal with them in a systematic and integrative way.

Manuel.grana en 13:04 7 ene 2013

2013-01-07T13:04:43Z

← Revisión anterior		Revisión del 15:04 7 ene 2013
Línea 11:		Línea 11:
	Such needs appear in apparently unrelated fields like motion understanding,		Such needs appear in apparently unrelated fields like motion understanding,
	content based image retrieval (CBIR)		content based image retrieval (CBIR)
	:- Combining multi-modal information which can not be subject of conventional error measures, impeding the application of state-of-the-art error minimization		:- Combining multi-modal information which can not be subject of conventional error measures, impeding the application of state-of-the-art error minimization approaches. The sources of information are heterogeneous, and its combination leads to unmeasurable data types. For instance, in the field of biomedical information processing, a wide diversity information sources may be used to reach a diagnosis that can be validated by the human operator.
	approaches. The sources of information are heterogeneous, and its combination leads to unmeasurable data types. For instance, in the field of biomedical
	information processing, a wide diversity information sources may be used to reach a diagnosis that can be validated by the human operator.
	:- New views on uncertainty, ambiguity and data artifacts (i.e. noise, missing data)		:- New views on uncertainty, ambiguity and data artifacts (i.e. noise, missing data)
	allowing to effectively deal with them in a systematic and integrative way.		allowing to effectively deal with them in a systematic and integrative way.

Manuel.grana en 13:04 7 ene 2013

2013-01-07T13:04:27Z

← Revisión anterior		Revisión del 15:04 7 ene 2013
Línea 11:		Línea 11:
	Such needs appear in apparently unrelated fields like motion understanding,		Such needs appear in apparently unrelated fields like motion understanding,
	content based image retrieval (CBIR)		content based image retrieval (CBIR)
	:- Combining multi-modal information which can not be subject of conventional		:- Combining multi-modal information which can not be subject of conventional error measures, impeding the application of state-of-the-art error minimization
	error measures, impeding the application of state-of-the-art error minimization		approaches. The sources of information are heterogeneous, and its combination leads to unmeasurable data types. For instance, in the field of biomedical
	approaches. The sources of information are heterogeneous, and its combination		information processing, a wide diversity information sources may be used to reach a diagnosis that can be validated by the human operator.
	leads to unmeasurable data types. For instance, in the field of biomedical
	information processing, a wide diversity information sources may be used to
	reach a diagnosis that can be validated by the human operator.
	:- New views on uncertainty, ambiguity and data artifacts (i.e. noise, missing data)		:- New views on uncertainty, ambiguity and data artifacts (i.e. noise, missing data)
	allowing to effectively deal with them in a systematic and integrative way.		allowing to effectively deal with them in a systematic and integrative way.

Manuel.grana en 18:18 30 nov 2012

2012-11-30T18:18:37Z

← Revisión anterior		Revisión del 20:18 30 nov 2012
Línea 54:		Línea 54:


	Submission deadline: ~~1st~~ December 2012		Submission deadline: 31st December 2012

Manuel.grana en 20:06 25 oct 2012

2012-10-25T20:06:34Z

← Revisión anterior		Revisión del 22:06 25 oct 2012
Línea 1:		Línea 1:
	;Title of the special issue: Innovative knowledge based techniques in pattern recognition		;Title of the special issue: Innovative knowledge based techniques in pattern recognition

	;~~Sumbission~~: go to journal site : select article type "special issue KES"		;Submission: go to journal submission site [http://ees.elsevier.com/prletters/default.asp]
			: select article type "special issue : KES"

	;Definition of issue’s scope:		;Definition of issue’s scope:

Manuel.grana en 20:04 25 oct 2012

2012-10-25T20:04:57Z

← Revisión anterior		Revisión del 22:04 25 oct 2012
Línea 1:		Línea 1:
	;Title of the special issue: Innovative knowledge based techniques in pattern recognition		;Title of the special issue: Innovative knowledge based techniques in pattern recognition

	;~~Acronym~~: KES		;Sumbission: go to journal site : select article type "special issue KES"

	;Definition of issue’s scope:		;Definition of issue’s scope:

Manuel.grana en 17:09 30 may 2012

2012-05-30T17:09:13Z

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2012-05-30T17:07:12Z

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Title of the special issue: Innovative knowledge based techniques in pattern recognition
Acronym: KES

Definition of issue’s scope:

This special issue identifies the following knowledge related challenges in the current
field of pattern recognition:
- Combining high (semantic) and low level information for pattern recognition.
Such needs appear in apparently unrelated fields like motion understanding,
content based image retrieval (CBIR)
- Combining multi-modal information which can not be subject of conventional
error measures, impeding the application of state-of-the-art error minimization
approaches. The sources of information are heterogeneous, and its combination
leads to unmeasurable data types. For instance, in the field of biomedical
information processing, a wide diversity information sources may be used to
reach a diagnosis that can be validated by the human operator.
- New views on uncertainty, ambiguity and data artifacts (i.e. noise, missing data)
allowing to effectively deal with them in a systematic and integrative way.
These challenges are currently being addressed from a number of points of view. The
development of semantic based pattern recognition systems, able to learn ontologies
from data, even heterogeneous data, as well as of ontology based reasoning allow for
the interplay between high-level semantics and low-level features. Hybrid systems
combining ontologies with fuzzy systems allow introduce uncertain modeling and
reasoning in the semantic domain. Lattice computing approaches allow seamlessly
treatment of heterogeneous data through lattice theory, while allowing new more robust
reasoning process diverging from the conventional statistics framework. Still, despite a
long tradition of research and results, new classification algorithms are being extended
to deal with heterogenous, ambiguous and artifact prone data. Also new ways of
interactive development of systems, such as active learning or reinforcement feedback
approaches can help to improve the efficiency of learning under scarce or expensive
data collection. The special issue invites works on these areas showing the interplay
between high level semantics and low level statistical and bio-inspired data processing
techniques.

Specific technical topics
- Ontology reasoning and learning
- Fuzzy and probabilistic ontologies
- Lattice computing approaches
- Knowledge based adaptive approaches
- Active learning, reinforcement learning in qualitative spaces
- Advanced classification systems (ensembles, ELM, clustering)
- Interplay of high level semantics and low level machine learning
Applications
- Remote sensing
- Multimedia information processing
- Biometrics, such as face recognition
- Fusion of clinical and medical image information
- Content based image retrival
- Multimodal information processing

Submission deadline: 1st December 2012

← Revisión anterior		Revisión del 19:09 30 may 2012
Línea 1:		Línea 1:
	Title of the special issue: Innovative knowledge based techniques in pattern recognition		;Title of the special issue: Innovative knowledge based techniques in pattern recognition
	Acronym: KES
			;Acronym: KES

	Definition of issue’s scope:		;Definition of issue’s scope:

	This special issue identifies the following knowledge related challenges in the current		This special issue identifies the following knowledge related challenges in the current
	field of pattern recognition:		field of pattern recognition:
	- Combining high (semantic) and low level information for pattern recognition.		:- Combining high (semantic) and low level information for pattern recognition.
	Such needs appear in apparently unrelated fields like motion understanding,		Such needs appear in apparently unrelated fields like motion understanding,
	content based image retrieval (CBIR)		content based image retrieval (CBIR)
	- Combining multi-modal information which can not be subject of conventional		:- Combining multi-modal information which can not be subject of conventional
	error measures, impeding the application of state-of-the-art error minimization		error measures, impeding the application of state-of-the-art error minimization
	approaches. The sources of information are heterogeneous, and its combination		approaches. The sources of information are heterogeneous, and its combination
Línea 15:		Línea 16:
	information processing, a wide diversity information sources may be used to		information processing, a wide diversity information sources may be used to
	reach a diagnosis that can be validated by the human operator.		reach a diagnosis that can be validated by the human operator.
	- New views on uncertainty, ambiguity and data artifacts (i.e. noise, missing data)		:- New views on uncertainty, ambiguity and data artifacts (i.e. noise, missing data)
	allowing to effectively deal with them in a systematic and integrative way.		allowing to effectively deal with them in a systematic and integrative way.


	These challenges are currently being addressed from a number of points of view. The		These challenges are currently being addressed from a number of points of view. The
	development of semantic based pattern recognition systems, able to learn ontologies		development of semantic based pattern recognition systems, able to learn ontologies
Línea 33:		Línea 36:
	techniques.		techniques.

	Specific technical topics		;Specific technical topics
	- Ontology reasoning and learning		:- Ontology reasoning and learning
	- Fuzzy and probabilistic ontologies		:- Fuzzy and probabilistic ontologies
	- Lattice computing approaches		:- Lattice computing approaches
	- Knowledge based adaptive approaches		:- Knowledge based adaptive approaches
	- Active learning, reinforcement learning in qualitative spaces		:- Active learning, reinforcement learning in qualitative spaces
	- Advanced classification systems (ensembles, ELM, clustering)		:- Advanced classification systems (ensembles, ELM, clustering)
	- Interplay of high level semantics and low level machine learning		:- Interplay of high level semantics and low level machine learning
	Applications		;Applications
	- Remote sensing		:- Remote sensing
	- Multimedia information processing		:- Multimedia information processing
	- Biometrics, such as face recognition		:- Biometrics, such as face recognition
	- Fusion of clinical and medical image information		:- Fusion of clinical and medical image information
	- Content based image retrival		:- Content based image retrival
	- Multimodal information processing		:- Multimodal information processing


	Submission deadline: 1st December 2012		Submission deadline: 1st December 2012