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Text Segmentation in Web Images Using Color Perception and Topological Features

Machine Learning Techniques for Multimedia pp Cite as. Unsupervised learning is very important in the processing of multimedia content as clustering or partitioning of data in the absence of class labels is often a requirement. This chapter begins with a review of the classic clustering techniques of k -means clustering and hierarchical clustering. Modern advances in clustering are covered with an analysis of kernel-based clustering and spectral clustering. One of the most popular unsupervised learning techniques for processing multimedia content is the self-organizing map, so a review of self-organizing maps and variants is presented in this chapter. The absence of class labels in unsupervised learning makes the question of evaluation and cluster quality assessment more complicated than in supervised learning.

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For the shortcoming of fuzzy c -means algorithm FCM needing to know the number of clusters in advance, this paper proposed a new self-adaptive method to determine the optimal number of clusters. Firstly, a density-based algorithm was put forward. The algorithm, according to the characteristics of the dataset, automatically determined the possible maximum number of clusters instead of using the empirical rule and obtained the optimal initial cluster centroids, improving the limitation of FCM that randomly selected cluster centroids lead the convergence result to the local minimum. Secondly, this paper, by introducing a penalty function, proposed a new fuzzy clustering validity index based on fuzzy compactness and separation, which ensured that when the number of clusters verged on that of objects in the dataset, the value of clustering validity index did not monotonically decrease and was close to zero, so that the optimal number of clusters lost robustness and decision function. Then, based on these studies, a self-adaptive FCM algorithm was put forward to estimate the optimal number of clusters by the iterative trial-and-error process.

Breast cancer is the second largest cause of cancer deaths among women. At the same time, it is also among the most curable cancer types if it can be diagnosed early. This paper presents a novel hybrid intelligent method for recognition of breast cancer tumors. The proposed method includes three main modules: the feature extraction module, the classifier module, and the optimization module. In the feature extraction module, fuzzy features are proposed as the efficient characteristic of the patterns. In the classifier module, because of the promising generalization capability of support vector machines SVM , a SVM-based classifier is proposed.

It includes contributions from diverse areas of soft computing such as uncertain computation, Z-information processing, neuro-fuzzy approaches, evolutionary computing and others. The topics of the papers include theory of uncertainty computation; theory and application of soft computing; decision theory with imperfect information; neuro-fuzzy technology; image processing with soft computing; intelligent control; machine learning; fuzzy logic in data analytics and data mining; evolutionary computing; chaotic systems; soft computing in business, economics and finance; fuzzy logic and soft computing in the earth sciences; fuzzy logic and soft computing in engineering; soft computing in medicine, biomedical engineering and the pharmaceutical sciences; and probabilistic and statistical reasoning in the social and educational sciences. The book covers new ideas from theoretical and practical perspectives in economics, business, industry, education, medicine, the earth sciences and other fields. In addition to promoting the development and application of soft computing methods in various real-life fields, it offers a useful guide for academics, practitioners, and graduates in fuzzy logic and soft computing fields. Skip to main content Skip to table of contents. Advertisement Hide. This service is more advanced with JavaScript available.

Unsupervised Learning and Clustering

Clustering is the process of partitioning elements into a number of groups clusters such that elements in the same cluster are more similar than elements in different clusters. Clustering has been applied in a wide variety of fields, ranging from medical sciences, economics, computer sciences, engineering, social sciences, to earth sciences [1,2], reflecting its important role in scientific research. With several hundred clustering methods in existence [3], there is clearly no shortage of clustering algorithms but, at the same time, satisfactory answers to some basic questions are still to come.

Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group called a cluster are more similar in some sense to each other than to those in other groups clusters. It is a main task of exploratory data mining , and a common technique for statistical data analysis , used in many fields, including pattern recognition , image analysis , information retrieval , bioinformatics , data compression , computer graphics and machine learning. Cluster analysis itself is not one specific algorithm , but the general task to be solved. It can be achieved by various algorithms that differ significantly in their understanding of what constitutes a cluster and how to efficiently find them. Popular notions of clusters include groups with small distances between cluster members, dense areas of the data space, intervals or particular statistical distributions.

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The fuzzy clustering algorithm has been widely used in the research area and production and life.

To organize the wide variety of data sets automatically and acquire accurate classification, this paper presents a modified fuzzy c -means algorithm SP-FCM based on particle swarm optimization PSO and shadowed sets to perform feature clustering. SP-FCM introduces the global search property of PSO to deal with the problem of premature convergence of conventional fuzzy clustering, utilizes vagueness balance property of shadowed sets to handle overlapping among clusters, and models uncertainty in class boundaries. This new method uses Xie-Beni index as cluster validity and automatically finds the optimal cluster number within a specific range with cluster partitions that provide compact and well-separated clusters. Experiments show that the proposed approach significantly improves the clustering effect. Clustering is the process of assigning a homogeneous group of objects into subsets called clusters, so that objects in each cluster are more similar to each other than objects from different clusters based on the values of their attributes [ 1 ].

Три миллиона процессоров работали параллельно - считая с неимоверной скоростью, перебирая все мыслимые комбинации символов. Надежда возлагалась на то, что шифры даже с самыми длинными ключами не устоят перед исключительной настойчивостью ТРАНСТЕКСТА. Этот многомиллиардный шедевр использовал преимущество параллельной обработки данных, а также некоторые секретные достижения в оценке открытого текста для определения возможных ключей и взламывания шифров. Его мощь основывалась не только на умопомрачительном количестве процессоров, но также и на достижениях квантового исчисления - зарождающейся технологии, позволяющей складировать информацию в квантово-механической форме, а не только в виде двоичных данных. Момент истины настал в одно ненастное октябрьское утро.

И тогда она вспомнила. Дэвид. Паника заставила Сьюзан действовать. У нее резко запершило в горле, и в поисках выхода она бросилась к двери. Переступив порог, она вовремя успела ухватиться за дверную раму и лишь благодаря этому удержалась на ногах: лестница исчезла, превратившись в искореженный раскаленный металл.

 Да нет, сэр, - попыталась она сгладить неловкость.  - Не в этом дело… - Да в этом.  - Он все еще посмеивался.

2 Comments

EneyГ©n A. 17.03.2021 at 21:05

PDF | The adoption of triangular fuzzy sets to define Strong Fuzzy Partitions (​points of separation between cluster projections on eration of a well-formed triangular fuzzy set (red In IEEE, editor, 18th International Conference or compactness–separability, do not allow to find the optimal partition.

Yanella R. 18.03.2021 at 21:44

David J.

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