標(biāo)題: Titlebook: Computational Intelligence Based on Lattice Theory; Vassilis G. Kaburlasos,Gerhard X. Ritter Book 2007 Springer-Verlag Berlin Heidelberg 2 [打印本頁(yè)] 作者: CK828 時(shí)間: 2025-3-21 16:48
書(shū)目名稱(chēng)Computational Intelligence Based on Lattice Theory影響因子(影響力)
書(shū)目名稱(chēng)Computational Intelligence Based on Lattice Theory影響因子(影響力)學(xué)科排名
書(shū)目名稱(chēng)Computational Intelligence Based on Lattice Theory網(wǎng)絡(luò)公開(kāi)度
書(shū)目名稱(chēng)Computational Intelligence Based on Lattice Theory網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱(chēng)Computational Intelligence Based on Lattice Theory被引頻次
書(shū)目名稱(chēng)Computational Intelligence Based on Lattice Theory被引頻次學(xué)科排名
書(shū)目名稱(chēng)Computational Intelligence Based on Lattice Theory年度引用
書(shū)目名稱(chēng)Computational Intelligence Based on Lattice Theory年度引用學(xué)科排名
書(shū)目名稱(chēng)Computational Intelligence Based on Lattice Theory讀者反饋
書(shū)目名稱(chēng)Computational Intelligence Based on Lattice Theory讀者反饋學(xué)科排名
作者: Preserve 時(shí)間: 2025-3-21 23:20
Computational Intelligence Based on Lattice Theory作者: fatuity 時(shí)間: 2025-3-22 02:24 作者: bizarre 時(shí)間: 2025-3-22 05:26 作者: AND 時(shí)間: 2025-3-22 08:49 作者: Conflagration 時(shí)間: 2025-3-22 16:48 作者: Conflagration 時(shí)間: 2025-3-22 17:09
https://doi.org/10.1007/978-94-017-3081-5a new vector-based approach for the extension of MM for greyscale images to colour morphology. We will extend the basic morphological operators dilation and erosion based on the threshold and fuzzy set approach to colour images. Finally in the last section we illustrate an image denoising method usi作者: 扔掉掐死你 時(shí)間: 2025-3-23 00:27 作者: 流動(dòng)性 時(shí)間: 2025-3-23 05:04
Learning in Lattice Neural Networks that Employ Dendritic Computingron’s membrane. Neuroscientists now believe that the basic computation units are dendrites, capable of computing simple logic functions. This paper discusses two types of neural networks that take advantage of these new discoveries. The focus of this paper is on some learning algorithms in the two n作者: 我沒(méi)有命令 時(shí)間: 2025-3-23 07:40
Orthonormal Basis Lattice Neural Networksorld problems. In this chapter a novel model of a lattice neural network (LNN) is presented. This new model generalizes the standard basis lattice neural network (SB-LNN) based on dendritic computing. In particular, we show how each neural dendrite can work on a different orthonormal basis than the 作者: 痛恨 時(shí)間: 2025-3-23 09:58 作者: Density 時(shí)間: 2025-3-23 13:59 作者: Bumble 時(shí)間: 2025-3-23 19:59
Convex Coordinates From Lattice Independent Sets for Visual Pattern Recognitioncs and data mining. The purpose of this chapter is to introduce a new feature extraction process based on the detection of extremal points on the cloud of points that represent the high dimensional data sample. These extremal points are assumed to de.ne an approximation to the convex hull covering t作者: NIP 時(shí)間: 2025-3-24 00:50
A Lattice-Based Approach to Mathematical Morphology for Greyscale and Colour Imageslation. MM has many applications in image analysis such as edge detection, noise removal, object recognition, pattern recognition and image segmentation in a.o. geosciences, materials science, the biological and medical world [13, 15]. MM was originally developed for binary images only. The basic to作者: Angiogenesis 時(shí)間: 2025-3-24 06:23
Morphological and Certain Fuzzy Morphological Associative Memories for Classification and Predictionight into the storage and recall phases of gray-scale autoassociative memories. This article extends these results to the heteroassociative and to the fuzzy case in view of the fact that a gray-scale MAM model can be converted into a fuzzy MAM model that coincides with the Lukasiewicz IFAM by applyi作者: Insulin 時(shí)間: 2025-3-24 06:57 作者: 2否定 時(shí)間: 2025-3-24 10:51
Machine Learning Techniques for Environmental Data Estimationthe historical data containing wind parameter values at two other di.erent locations. We evaluate the performance of two signi.cant machine learning methodologies, the Fuzzy Lattice Neurocomputing (FLN) and the Support Vector Regression (SVR). The results of the speci.c applications are compared wit作者: Cupping 時(shí)間: 2025-3-24 18:08
Application of Fuzzy Lattice Neurocomputing (FLN) in Ocean Satellite Images for Pattern Recognitionizes the most important . in satellite AVHRR (Advanced Very High Resolution Radiometer) images. This chapter presents a hybrid model based on an expert system segmentation method, a method of correlation-based feature selection, and a few classi.ers including Bayesian nets (BN) and fuzzy lattice neu作者: guzzle 時(shí)間: 2025-3-24 22:47 作者: 冷峻 時(shí)間: 2025-3-24 23:25
Fuzzy Lattice Reasoning (FLR) Classification Using Similarity Measuresilarity and distance measures often used in cluster analysis. We show that for the cosine similarity measures, we can weigh the contribution of each attribute found in the data set. Furthermore, we show that evolutionary algorithms such as genetic algorithms, tabu search, particle swarm optimization作者: Incommensurate 時(shí)間: 2025-3-25 06:15 作者: 結(jié)構(gòu) 時(shí)間: 2025-3-25 11:10 作者: 小隔間 時(shí)間: 2025-3-25 15:30
Evaluation of Streamflow Forecasting Models an energy function minimizer. Moreover, we can introduce tunable nonlinearities. The interest here is in classiffication applications. We cite evidence that the proposed techniques can clearly improve performance.作者: 厚顏無(wú)恥 時(shí)間: 2025-3-25 17:48
A Panorama of Stochastic-Process Limits, an inclusion measure on the Boolean lattice of logical statements leads to Bayesian probability theory, which suggests a fundamental relationship between fuzzification of a Boolean lattice and Bayesian probability theory.作者: 思考 時(shí)間: 2025-3-25 21:53 作者: Indent 時(shí)間: 2025-3-26 00:20
Valuations on Lattices: Fuzzification and its Implications an inclusion measure on the Boolean lattice of logical statements leads to Bayesian probability theory, which suggests a fundamental relationship between fuzzification of a Boolean lattice and Bayesian probability theory.作者: 表臉 時(shí)間: 2025-3-26 06:46 作者: 老人病學(xué) 時(shí)間: 2025-3-26 10:17 作者: GUISE 時(shí)間: 2025-3-26 16:04 作者: fluffy 時(shí)間: 2025-3-26 17:04 作者: 迎合 時(shí)間: 2025-3-26 21:56 作者: 單獨(dú) 時(shí)間: 2025-3-27 03:01
Convex Coordinates From Lattice Independent Sets for Visual Pattern Recognitionhe data sample points. The features extracted are the coordinates of the data points relative to the extremal points, the convex coordinates. We have experimented this approach in several applications that will be summarized in the chapter.作者: 刺耳 時(shí)間: 2025-3-27 08:56
Application of Fuzzy Lattice Neurocomputing (FLN) in Ocean Satellite Images for Pattern Recognitionral networks. The results obtained by the fuzzy lattice system are clearly better than the results obtained by ANNs (Artificial Neural Nets), knowledge based reasoning systems, and graphic expert system (GES).作者: 致命 時(shí)間: 2025-3-27 10:54 作者: 自制 時(shí)間: 2025-3-27 14:26 作者: 觀點(diǎn) 時(shí)間: 2025-3-27 17:46 作者: DEVIL 時(shí)間: 2025-3-27 23:35 作者: 華而不實(shí) 時(shí)間: 2025-3-28 05:00
Unmatched Jumps in the Limit Process,al effort to produce an optimal or a sub-optimal network. Furthermore, we compare the performance of GFAM, GEAM and GGAM with other competitive ARTMAP structures that have appeared in the literature and addressed the category proliferation problem in ART.作者: eulogize 時(shí)間: 2025-3-28 08:06
More Stochastic-Process Limits,, and differential evolution can be used to weigh the importance of each attribute and that this weighting can provide additional improvements over simply using the similarity measure. We present experimental evidence that the proposed techniques imply significant improvements.作者: confederacy 時(shí)間: 2025-3-28 12:36
1860-949X ntary material: A number of di?erent instruments for design can be uni?ed in the context of lattice theory towards cross-fertilization By“l(fā)atticetheory”[1]wemean,equivalently,eitherapartialordering relation [2,3]ora couple of binary algebraic operations [3, 4]. There is a growing interest in computa作者: 換話(huà)題 時(shí)間: 2025-3-28 16:24 作者: Finasteride 時(shí)間: 2025-3-28 22:36 作者: 膠狀 時(shí)間: 2025-3-29 02:14 作者: 套索 時(shí)間: 2025-3-29 06:41
A. W. Heemink,H. F. P. Van Den Boogaardethodologies, the Fuzzy Lattice Neurocomputing (FLN) and the Support Vector Regression (SVR). The results of the speci.c applications are compared with past work on the same data set, and a discussion upon the exhibited features is carried out.作者: cornucopia 時(shí)間: 2025-3-29 07:31
More on the Mathematical Framework,ness and uncertainty and the few examples that we can find are used by a minority. To extend a popular system (which many programmers are using) with the ability of combining crisp and fuzzy knowledge representations seems to be an interesting issue.作者: 隱士 時(shí)間: 2025-3-29 14:44
Learning in Lattice Neural Networks that Employ Dendritic Computingscusses two types of neural networks that take advantage of these new discoveries. The focus of this paper is on some learning algorithms in the two neural networks. Learning is in terms of lattice computations that take place in the dendritic structure as well as in the cell body of the neurons used in this model.作者: 使人入神 時(shí)間: 2025-3-29 17:08 作者: dysphagia 時(shí)間: 2025-3-29 19:55
Morphological and Certain Fuzzy Morphological Associative Memories for Classification and Prediction fuzzy case in view of the fact that a gray-scale MAM model can be converted into a fuzzy MAM model that coincides with the Lukasiewicz IFAM by applying an appropriate threshold. The article includes experimental results concerning applications of MAM and fuzzy MAM models in classiffication and prediction.作者: 類(lèi)型 時(shí)間: 2025-3-30 02:18 作者: 暫停,間歇 時(shí)間: 2025-3-30 06:31
Fuzzy Prolog: Default Values to Represent Missing Informationness and uncertainty and the few examples that we can find are used by a minority. To extend a popular system (which many programmers are using) with the ability of combining crisp and fuzzy knowledge representations seems to be an interesting issue.作者: Affirm 時(shí)間: 2025-3-30 11:45 作者: 表示向下 時(shí)間: 2025-3-30 13:40
B. Bass,G. Huang,Y. Yin,S. J. Cohenron’s membrane. Neuroscientists now believe that the basic computation units are dendrites, capable of computing simple logic functions. This paper discusses two types of neural networks that take advantage of these new discoveries. The focus of this paper is on some learning algorithms in the two n作者: 大方一點(diǎn) 時(shí)間: 2025-3-30 17:52
Seeking User Input in Inflow Forecastingorld problems. In this chapter a novel model of a lattice neural network (LNN) is presented. This new model generalizes the standard basis lattice neural network (SB-LNN) based on dendritic computing. In particular, we show how each neural dendrite can work on a different orthonormal basis than the 作者: Mumble 時(shí)間: 2025-3-30 21:20
Water Science and Technology Libraryzed lattice, or category, in which the concepts are related in hierarchical fashion by lattice-like links called concept morphisms. A concept morphism describes how an abstract concept can be used within a more specialized concept in more than one way as with “color”, which can appear in “apples” as作者: 無(wú)王時(shí)期, 時(shí)間: 2025-3-31 01:26 作者: strdulate 時(shí)間: 2025-3-31 06:13
T. Masumoto,H. Sato,K. Iwasaki,K. Shibuyacs and data mining. The purpose of this chapter is to introduce a new feature extraction process based on the detection of extremal points on the cloud of points that represent the high dimensional data sample. These extremal points are assumed to de.ne an approximation to the convex hull covering t作者: crutch 時(shí)間: 2025-3-31 10:00 作者: 玉米 時(shí)間: 2025-3-31 17:19 作者: crease 時(shí)間: 2025-3-31 19:12 作者: 切碎 時(shí)間: 2025-3-31 22:47
