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 this paper, a new recognition algorithm of SAR image which is based on combined templates has been proposed. The new algorithm is based on the traditional mean templates recognition. We use some statistical information of the training samples to make a refusing threshold, which is expected to have the ability that can refuse the non-template-class targets effectively. Meanwhile, the proposed combined...
Kernel Fisher discriminant analysis(KFDA) improves greatly the multi-classification accuracy of FDA via using kernel trick. The optimal kernel Fisher projection of KFDA can be expressed as a generalized characteristic equation. However, solving the characteristic equation is very difficult, then regularization method is used for it. In this paper, we develop a novel approach to perform regularization...
Development of modern technologies is related to an increasing complexity of the objects controled and hence the systems controlling them. In the most cases, automatic control systems consist of different nonlinear elements that significantly limit the capabilities of classical control theory in designing controllers. In recent decades, the methodology of neural networks has been increasingly used...
In this paper, we propose a dictionary updating method and show numerically that it can converge to a dictionary that outperforms the dictionary derived by the K-SVD method. The proposed method is based on the proximal point approach used in the convex optimization algorithm. We incorporate the approach into the well-known MOD and combine the result with the K-SVD method to obtain the proposed method...
This paper presents an iterative classification algorithm called Ridge-adjusted Slack Variable Optimization (RiSVO). RiSVO is an iterative procedure with two steps: (1) A working subset of the training data is selected so as to reject “extreme” patterns. (2) the decision vector and threshold value are obtained by minimizing the energy function associated with the slack variables. From a computational...
In a world of exponentially growing data and finite computing resources, rank learning methods can play a critical role in data prioritization. While a number of new rank learning algorithms have been developed, there is a relative paucity of work to generate bounds that characterize the performance of these algorithms. When such bounds have been developed, it has often proved difficult to apply them...
In this paper, we propose distribution based binary discriminative features and a novel feature enhancement process for automatic modulation classification. The new features exploit the signal distribution mismatch between two modulations. Signal distributions on I-Q segments, amplitude and phase, are considered to produce a comprehensive feature set for improved robustness. Logistic regression is...
Unsupervised models can provide supplementary soft constraints to help classify new data since similar instances are more likely to share the same class label. In this context, we investigate how to make an existing algorithm, named C3E (from Combining Classifier and Cluster Ensembles), more user-friendly by automatically tunning its main parameters with the use of metaheuristics. In particular, the...
Image segmentation is one of the fundamental problems in computer vision. In this work, we present a new segmentation algorithm that is based on the theory of two-dimensional hidden Markov models (2D-HMM). Unlike most 2D-HMM approaches we do not apply the Viterbi Algorithm, instead we present a computationally efficient algorithm that propagates the state probabilities through the image. This approach...
The Minimal Learning Machine (MLM) has been recently proposed as a novel supervised learning method for regression problems aiming at reconstructing the mapping between input and output distance matrices. Estimation of the response is then achieved from the geometrical configuration of the output points. Thanks to its comprehensive formulation, the MLM is inherently capable of dealing with nonlinear...
This paper presents some strategies used for creating intelligent players of rock-paper-scissors using WiSARD weightless neural networks and results obtained therewith. These strategies included: (i) a new approach for encoding of the input data, (ii) three new training algorithms that allow the reclassification of the input patterns over time, (iii) a method for dealing with incomplete information...
Oil (energy) is huge influence of economic Indonesia. Since many sectors from Industries until individual need it. In fact, Indonesia is a country with high density population. Because of that, the necessity of oil must be meet amount of inhabitant in Indonesia. If government failed to answer the demand of oil, then Indonesia will be face economic crisis for long-term. So that, the forecast of it...
Sparse Representation Classifiers and their variants are more and more used by computer vision and signal processing communities due to their good performance. Recently, it has been shown that the performance of Sparse Representation Classifiers and their variants in terms of accuracy and computational complexity can be enhanced by simply including a two-phase coding scheme regardless of the used...
Document Image Binarization is a technique to segment text out from the background region of a document image, which is a challenging task due to high intensity variations of the document foreground and background. Recently, a series of document image binarization contests (DIBCOs) had been held that have drawn great research interest in this area. Several document binarization techniques have been...
Following the philosophy of adaptive optimal control, a new technique is presented in this paper for robust optimal regulation of a class of nonlinear systems. First, accounting for a nominal system model, a single network adaptive critic (SNAC) based multi-layered neural network (called as NN1) is synthesized offline for optimal regulation of the nominal system. However, another linear-in-weight...
Semi-supervised Learning with Graphs can achieve good results in classification tasks even in difficult conditions. Unfortunately, it can be slow and use a lot of memory. The first important step of the graph-based semi-supervised learning approaches is the construction of the graph from the data, where each data-point usually becomes a vertex in the graph - a potential problem with large amounts...
In this paper, an iterative method for solving linear systems and min is used to calculate the best representations of the test sample as a linear combination of all the training samples. Then a least-squares Based two-phase face recognition algorithm is proposed. This algorithm is as follows: its first phase uses a least-squares method to calculate the contribution between a test sample and each...
Scalability to large numbers of classes is an important challenge for multi-class classification. It can often be computationally infeasible at test phase when class prediction is performed by using every possible classifier trained for each individual class. This paper proposes an attribute-based learning method to overcome this limitation. First is to define attributes and their associations with...
Since the beginning, some pattern recognition techniques have faced the problem of high computational burden for dataset learning. Among the most widely used techniques, we may highlight Support Vector Machines (SVM), which have obtained very promising results for data classification. However, this classifier requires an expensive training phase, which is dominated by a parameter optimization that...
Dimension reduction of data is an important theme in the data processing and on the web to represent and manipulate higher dimensional data. Reduct in the rough set is a minimal subset of features, which has almost the same discernible power as the entire features in the higher dimensional scheme. But, there are problems in the application of reducts for classification. Here, we develop a method which...
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.