«granular spaces»
۲۲۰۸ نتیجهGranular Space-Based Feature Selection and Its Applications
Feature selection is viewed as an important preprocessing step for pattern recognition, machine learning and data mining. Considering a consistency measure introduced in rough sets, the problem of feature selection aims to retain the discriminatory power of original features. Man
Class-dependent rough-fuzzy granular space, dispersion index and classification
A new rough-fuzzy model for pattern classification based on granular computing is described in the present article. In this model, we propose the formulation of class-dependent granules in fuzzy environment. Fuzzy membership functions are used to represent the feature-wise belong
Variable Granularity Space Filling Curve for Indexing Multidimensional Data
Efficiently accessing multidimensional data is a challenge for building modern database applications that involve many folds of data such as temporal, spatial, data warehousing, bio-informatics, etc. This problem stems from the fact that multidimensional data have no given order
Granularity, space and motion-framed location
The ways in which different languages encode motion events has been the topic of intense analysis and dissection in recent years, especially with regard to Talmy’s (1991, 2000) verb/satellite-framed typology. This chapter shifts course by moving away from motion event typologies
Rough-wavelet granular space and classification of multispectral remote sensing image
A new rough-wavelet granular space based model for land cover classification of multispectral remote sensing image, is described in the present article. In this model, we propose the formulation of classdependent (CD) granules in wavelet domain using shift-invariant wavelet trans
Special relativity induced by granular space
We show that the special relativistic dynamics, when combined with quantum mechanics and the concept of superstatistics, can be interpreted as arising from two interlocked non-relativistic stochastic processes that operate at different energy scales. This framework leads to Feynm
Adaptive neighborhood granularity selection and combination based on margin distribution optimization
Granular computing aims to develop a granular view for interpreting and solving problems. The model of neighborhood rough sets is one of effective tools for granular computing. This model can deal with complex tasks of classification learning. Despite the success of the neighborh
Multi-granularity distance metric learning via neighborhood granule margin maximization
Learning a distance metric from training samples is often a crucial step in machine learning and pattern recognition. Locality, compactness and consistency are considered as the key principles in distance metric learning. However, the existing metric learning methods just conside
Rule Generation Approach for Granular Computing Using Rough Mereology
Granular computing has been applied in many fields to solve problems and describe many spaces at different granularity and hierarchies. This paper proposes a rule generation approach based on granular computing in the frame of rough mereology. The proposed approaches generates a
Granularity-Based User-Centric Multi-Strategies and Application in Knowledge Retrieval
Granular computing is a general computing paradigm of problem solving for effectively using granules in problem solving. From the viewpoint of granularity, this paper presents a new granular computing data cycle model in which information granule construction, granular space, inf