The proceedings contain 98 papers. The topics discussed include: some comparative analyses of data in the RSDS system;rough temporal vague sets in pawlak approximation space;poset approaches to covering-based rough se...
ISBN:
(纸本)3642162479
The proceedings contain 98 papers. The topics discussed include: some comparative analyses of data in the RSDS system;rough temporal vague sets in pawlak approximation space;poset approaches to covering-based rough sets;knowledge reduction in random incomplete information systems via evidence theory;knowledge reduction based on granular computing from decision information systems;pattern classification using class-dependent rough-fuzzy granular space;incorporating great deluge with kempe chain neighbourhood structure for the enrolment-based course timetabling problem;ordered weighted average based fuzzy rough sets;on attribute reduction of rough set based on pruning rules;set-theoretic models of granular structures;a robust fuzzy rough set model based on minimum enclosing ball;and indiscernibility and similarity in an incomplete information table.
The importance of decision making problem in an imprecise environment is growing very significantly in recent *** object recognition from an imprecise multiobserver data has been presented *** apply the concept of int...
详细信息
The importance of decision making problem in an imprecise environment is growing very significantly in recent *** object recognition from an imprecise multiobserver data has been presented *** apply the concept of intuitionistic fuzzy soft sets in a decision making *** solve the problem with the help of 'similarity measurement' technique.
This paper presents a new image coding scheme based upon image patternrecognition (IPR) and discrete wavelet transforms (DWT). First, the original image is predicted by image patternrecognition, then the predicted e...
详细信息
ISBN:
(纸本)9781424465828
This paper presents a new image coding scheme based upon image patternrecognition (IPR) and discrete wavelet transforms (DWT). First, the original image is predicted by image patternrecognition, then the predicted error image is encoded by DWT, and the pattern library learning is based upon selforganizing maps (SOM) algorithm. To improve the performance of SOM algorithm, a frequency sensitive self-organizing feature maps (FSOM) algorithm is proposed. Experiment results show that the IPR-FSOMDWT coding scheme can get better coding performance than standard JPEG2000.
The proceedings contain 77 papers. The special focus in this conference is on applications of methods for information processing and management of uncertainty in knowledge-based systems. The topics include: Data-drive...
ISBN:
(纸本)9783642140570
The proceedings contain 77 papers. The special focus in this conference is on applications of methods for information processing and management of uncertainty in knowledge-based systems. The topics include: Data-driven design of Takagi-sugeno fuzzy systems for predicting NOx emissions;coping with uncertainty in temporal gene expressions using symbolic representations;olive trees detection in very high resolution images;soft concept hierarchies to summarise data streams and highlight anomalous changes;using enriched ontology structure for improving statistical models of gene annotation sets;obtaining the compatibility between musicians using softcomputing;consistently handling geographical user data;a model based on outranking for database preference queries;incremental membership function updates;a new approach for comparing fuzzy objects;the bipolar semantics of querying null values in regular and fuzzy databases;describing fuzzy DB schemas as ontologies;using textual dimensions in data warehousing processes;aggregation of partly inconsistent preference information;risk neutral valuations based on partial probabilistic information;a new contextual discounting rule for lower probabilities;the power average operator for information fusion;color recognition enhancement by fuzzy merging;multiagent decision making, fuzzy prevision, and consensus;a categorical approach to the extension of social choice functions;signatures for assessment, diagnosis and decision-making in ageing;a default risk model in a fuzzy framework;on a fuzzy weights representation for inner dependence AHP;a neuro-fuzzy decision support system for selection of small scale business;estimating the brazilian central bank's reaction function by fuzzy inference system;some notes on the value of vagueness in everyday communication;complexity and fuzziness in 20th century science and technology;educational software of fuzzy logic and control;a fuzzy distance between two fuzzy numbers;on the jaccard index w
This paper presents a novel local feature descriptor, the local directional pattern (LDP), for recognizing human face. A LDP feature is obtained by computing the edge response values in all eight directions at each pi...
详细信息
This paper presents a novel local feature descriptor, the local directional pattern (LDP), for recognizing human face. A LDP feature is obtained by computing the edge response values in all eight directions at each pixel position and generating a code from the relative strength magnitude. Each face is represented as a collection of LDP codes for the recognition process.
This paper develops a recursive method for computing moments of 2D objects described by elliptic Fourier descriptors (EFD). Green's theorem is utilized to transform 2D surface integrals into 1D line integrals and ...
详细信息
ISBN:
(纸本)9781424475421
This paper develops a recursive method for computing moments of 2D objects described by elliptic Fourier descriptors (EFD). Green's theorem is utilized to transform 2D surface integrals into 1D line integrals and EFD description is employed to derive recursions for moments computations. Experiments are performed to quantify the accuracy of our proposed method. Comparison with Bernstein-Bézier representations is also provided.
This paper issues the problem of fault diagnosis in high computing system. In order to solve this problem, i.e., correctly and efficiently detecting the anomaly nodes during the system operation, which is very similar...
详细信息
This paper issues the problem of fault diagnosis in high computing system. In order to solve this problem, i.e., correctly and efficiently detecting the anomaly nodes during the system operation, which is very similar to the principle of patternrecognition research work, thus we try to use some patternrecognition methods to analysis and solve fault diagnosis problem in this paper. And also we do some experiment and compare the results and finally get some useful conclusion to show that Kernel Eigenface and Kernel Fisherface methods achieve lower error rates than the ICA and PCA approaches in anomaly nodes detection.
The classification of sequences requires the combination of information from different time points. In this paper the detection of facial expressions is considered. Experiments on the detection of certain facial muscl...
详细信息
ISBN:
(纸本)9781424475421
The classification of sequences requires the combination of information from different time points. In this paper the detection of facial expressions is considered. Experiments on the detection of certain facial muscle activations in videos show that it is not always required to model the sequences fully, but that the presence of specific frames (the concept frame) can be sufficient for a reliable detection of certain facial expression classes. For the detection of these concept frames a standard classifier is often sufficient, although a more advanced clustering approach performs better in some cases.
The ubiquitous role of the cyber-infrastructures, such as the WWW, provides myriad opportunities for machine learning and its broad spectrum of application domains taking advantage of digital communication. pattern cl...
详细信息
ISBN:
(纸本)9781601321480
The ubiquitous role of the cyber-infrastructures, such as the WWW, provides myriad opportunities for machine learning and its broad spectrum of application domains taking advantage of digital communication. pattern classification and feature extraction are among the first applications of machine learning that have received extensive attention. The most remarkable achievements have addressed data sets of moderate-to-large size. The 'data deluge' in the last decade or two has posed new challenges for AI researchers to design new, effective and accurate algorithms for similar tasks using ultra-massive data sets and complex (natural or synthetic) dynamical systems. We propose a novel principled approach to feature extraction in hybrid architectures comprised of humans and machines in networked communication, who collaborate to solve a pre-assigned patternrecognition (feature extraction) task. There are two practical considerations addressed below: (I) Human experts, such as plant biologists or astronomers, often use their visual perception and other implicit prior knowledge or expertise without any obvious constraints to search for the significant features, whereas machines are limited to a pre-programmed set of criteria to work with;(2) in a team collaboration of collective problem solving, the human experts have diverse abilities that are complementary, and they learn from each other to succeed in cognitively complex tasks in ways that are still impossible imitate by machines. Thus, from an abstract viewpoint, in solving complex visual perception-cognition problems, a hybrid network of humans and machines could be far more powerful than a network of intelligent machine-only agents whose capabilities are bounded by what the present state of AI knowledge could offer. This article reports a preliminary progress towards theoretical foundations for HHML as a semi-programmable case belonging to the broader domain that we refer to as 'Collective Cognitive Systems'. We use a
暂无评论