The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
In classification problems, it is preferred to attack the discrimination problem directly rather than indirectly by first estimating the class densities and by then estimating the discrimination function from the generative models through Bayes's rule. Sometimes, however, it is convenient to express the models as probabilistic models, since they are generative in nature and can handle the representation...
Reactive power optimization has been a hard problem for years. However, the existed optimization algorithms are usually complex in calculation and not obvious in physical meaning. To study a simple and effective method for reactive power optimization, this paper puts forward a concept of optimal reactive load, and proposes a method called power circle method. With power circles, this paper presents...
In this paper, we consider the adaptation of two Partial Differential Equations (PDEs) on weighted graphs, p-Laplacian and eikonal equations, for semi-supervised classification tasks. These equations are a discrete analogue of well known geometric PDEs, which are widely used in image processing. While the p-Laplacian on graphs was intensively used in data classification, few works relate to the eikonal...
Association is widely used to find relations among items in a given database. However, finding the interesting patterns is a challenging task due to the large number of rules that are generated. Traditionally, this task is done by post-processing approaches that explore and direct the user to the interesting rules of the domain. Some of these approaches use the user's knowledge to guide the exploration...
Extraction of interesting negative association rules from large data sets is measured as a key feature of data mining. Many researchers discovered numerous algorithms and methods to find out negative and positive association rules. From the existing approaches, the frequent pattern growth (FP-Growth) approach is well-organized and capable method for finding the item sets which are frequent, without...
This paper presents a new feature selection technique based on rough sets and bat algorithm (BA). BA is attractive for feature selection in that bats will discover best feature combinations as they fly within the feature subset space. Compared with GAs, BA does not need complex operators such as crossover and mutation, it requires only primitive and simple mathematical operators, and is computationally...
This paper proposes a new approach for clustering English text documents, based on finding the pair wise correlation of documents in a given set of text documents. The correlation coefficient for each pair of documents is calculated on the basis of ranks given to the words in the documents. The ranking of the words occurring in a document is computed on the basis of weights of the words calculated...
Classification process is one of the most important operations implemented on the huge data warehouses in order to classify the data. Availability of huge amounts of data increased the need for effective techniques to analyze and classify data accurately. Many algorithms in the field of swarm intelligence are able to contribute to improve the classification accuracy using the optimal algorithm methods...
With the development of Web2.0, human beings are no longer simple units in the Internet world. Web of Things (WoT) system makes devices and humans connect to each other, and gradually becomes the leading force of information creation, transmission and acquisition. However, the widely network applications and information circulation in recent years have resulted in information overload, which means...
Human activity detection from videos is very challenging, and has got numerous applications in sports evalution, video surveillance, elder/child care, etc. In this research, a model using sparse representation is presented for the human activity detection from the video data. This is done using a linear combination of atoms from a dictionary and a sparse coefficient matrix. The dictionary is created...
Analysing public sentiment about future events, such as demonstration or parades, may provide valuable information while estimating the level of disruption and disorder during these events. Social media, such as Twitter or Facebook, provides views and opinions of users related to any public topics. Consequently, sentiment analysis of social media content may be of interest to different public sector...
In this study 2-layer ANN (artificial neural network) a linear classifier and k-NN (k-nearest neighbor) a non-linear classifier were applied for identification and classification of images of four Indian wheat seed species into four classes of wheat seeds on the basis of their varieties. 120 images (40 images of four classes, 10 images of each class) from three different places were taken under same...
In this paper, we assume that a single pedestrian is a single blob, and define the representative properties of blobs to selection between merge and split of blobs. And we collect attribute information of the blob and make decision tree by using the ID3 algorithm. Next, we experimented for dividing the decision tree by directing the status of merge and split, as a result, it is possible to generate...
This work addresses position update mechanisms that may increase the accuracy of particle swarm classification (PSC), a derivative of Particle Swarm Optimization (PSO) fit for classification problems. The main idea in PSC is to retrieve the best particle positions corresponding to the centroids of classes. We present two variants of the PSC algorithm with different position update mechanisms. In particular,...
Along with the information explosion in the Internet era, the traditional classification methods, such as KNN (k-nearest neighbor), Naive Bayes (NB), encounter bottlenecks due to the endless stream of new words. In this paper, through comparing with the Rocchio and Bayesian algorithms, it has been found that centroid-based algorithms are insufficient for text classification. Therefore, a novel feature...
Sparse representation has attracted great attention in past years. Sparse Representation-based Classification (SRC) algorithm was developed and successfully used in face recognition. However, the importance of sparsity is much emphasized in SRC and the use of collaborative representation (CR) in SRC is ignored. In reality, it is the collaborative representation but not the l1-norm sparsity that makes...
This paper presents a hyperspectral image classification method based on the semi-supervised random forest (SSRF) algorithm. This method uses Deterministic Annealing (DA) and the random forest classifier (RFC). The first step consists of performing the random forest algorithm by using labeled data. Then, image is classified and the probability of each unlabeled data will be computed. Based on the...
Image matching-aided navigation system has broad application prospects, according to its prominent advantages in autonomy and high precision. In order to meet the demands of real time and accuracy of image aided navigation system, the paper propose a subpixel precision image matching algorithm by using a method called the four-dimension real matrix. At first this paper converts the transformation...
Classification is one of the problems in pattern recognition. Most of the time this problem will deal with data sets that are in numeric form and represented by vectors of numbers. Since there might be uncertainties embedded in a data set, it is more natural to represent the data set as fuzzy vectors. Hence, in this paper, we develop a fuzzy perceptron with pocket algorithm for fuzzy vectors. This...
Recently, clustering has been used for preprocessing datasets before applying classification algorithms in order to enhance classification efficiency. A strong clustered dataset as input to classification algorithms can significantly improve computation time. This can be particularly useful in Big Data where computation time is equally or more important than accuracy. However, there is a trade-off...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.