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With the rapid development of microblog, the research of topic detection has been paid more attention, which has begun to transfer from the traditional news media to microblog. However, compared with the traditional fields where topic detection applied, the microblogging data is written informally and the structure of that is not rigorous, which will bring great difficulties. In the process of topic...
Topic identification (TID) is a technique associated with labelling a set of textual documents with a meaningful label representing its content. TID for online news presents different problems from TID for other corpora, such as the large data volume and the frequently updated topic. Moreover, the number of developing methods for Indonesian corpus is rather small. Brace well's algorithm has been proven...
It is important for security surveillance systems to operate continuously for 24 hours. During the night, use of far-infrared cameras is preferable in outdoor situations due to a number of reasons. However, the person in the image is often unrecognizable. This paper attempts to estimate the face from his/her far-infrared image. The estimation is done through two phases, a holistic estimation and a...
In this paper, we consider the issue of computing low rank (LR) recovery of matrices with sparse errors. Based on the success of low rank matrix recovery in statistical learning, computer vision and signal processing, a novel low rank matrix recovery algorithm with Fisher discrimination regularization (FDLR) is proposed. Standard low rank matrix recovery algorithm decomposes the original matrix into...
Detecting the banknote serial number is an important task in business transaction. In this paper, we propose a new banknote number recognition method. The preprocessing of each banknote image is used to locate position of the banknote number image. Each number image is divided into non-overlapping partitions and the average gray value of each partition is used as feature vector for recognition. The...
In this paper, we identify and analyze the tradeoff between security and reliability for wireless communications in the presence of eavesdropping attack. Typically, we assume that both destination and the eavesdropper can only obtain channel state information with estimation errors, instead of perfect channel estimation predominantly assumed in the literature. Linear minimum mean-square error estimation...
Deep neural networks comprise several hidden layers of units, which can be pre-trained one at a time via an unsupervised greedy approach. A whole network can then be trained (fine-tuned) in a supervised fashion. One possible pre-training strategy is to regard each hidden layer in the network as the input layer of an auto-encoder. Since auto-encoders aim to reconstruct their own input, their training...
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...
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