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Traditional classifiers in steganalysis are no longer applicable when faced with massive feature set and high-dimensional sample set. We proposed a kind of selective ensemble classifier for universal steganalysis based on virus-evolutionary genetic algorithm. After generating some base learners, we selected some of them according to genetic optimization with an additonal virus population. The final...
In this paper we report on the performance of a coupled oscillator based implementation of the HMAX image-processing pipeline. Within this pipeline we have used coupled oscillator arrays to replace traditional Boolean logic with a Degree-of-Match (DoM) function that measures the L2 distance squared between two vectors in an n-dimensional space. We show that this operation can be used in three stages...
On-line learning for fuzzy classifiers are investigated in this paper. In the on-learning problems, it is assumed that there are only a single training patterns available at a time. This paper extends the assumption by allowing multiple training patterns available for incrementally update the fuzzy classifiers. Confidence-weighted learning is used for the on-line learning of the fuzzy classifiers...
This paper proposes a novel approach for automatic estimation of four important traits of speakers, namely age, height, weight and smoking habit, from speech signals. In this method, each utterance is modeled using the i-vector framework which is based on the factor analysis on Gaussian Mixture Model (GMM) mean supervectors, and the Non-negative Factor Analysis (NFA) framework which is based on a...
We consider the problem of automatically recognizing human faces in which sparse representation-based classification (SRC) offers a key. SRC includes two steps: seeking sparest solution and making decision by dictionary classifier (DC). Aiming at improving the performance of face recognition, this paper proposes a joint classification approach based on sparse representation. We initialize dictionary...
Sentiment classification is the main and popular task in the field of sentiment analysis. Most of the existing researches focus on how to extract the effective features, such as lexical features and syntactic features, while limited work has been done on the extraction of semantic features, which can make more contributions to sentiment classification. This paper presents a method for sentiment classification...
Machine translation (MT) has been developed and has achieved wide successes over last years. But this technology is still not able to deliver high quality translation and therefore post-editing is needed. Since post-editing could be time consuming even more than the translation process, having a quality estimation of the translated parts can be very useful. It means we need to estimate the confidence...
The current trend to use Brain-Computer Interfaces (BCIs) with mobile devices mandates the development of efficient EEG data processing methods. In this paper, we demonstrate the performance of a Principal Component Analysis (PCA) ensemble classifier for P300-based spellers. We recorded EEG data from multiple subjects using the Emotiv neuroheadset in the context of a classical oddball P300 speller...
Much research has been done on named entity recognition such as whether the name is a person, company or place, and valuable contributions have been made. However, there has been little research on country recognition of people's names and places. In this paper, we develop a classification technique for social multimedia to automatically classify countries for person or place. This technique will...
Along with the rapid improvements of informational technology, educational data grows quickly. Such data become massive and raw data. Researchers develop educational standards to regular such data. However, the standards are multiple and the education resources based on different education standards have different structure, which is hard to be shared. Most of them have become Information Islands...
Remote sensing images are relevant materials for observation and thematic mapping by multispectral and multi-textural classification. In this paper, we propose classification of urban data with high spectral and spatial resolution. The approach is based on building Differential Morphological Profile (DMP) and then classifying each pixel using Support Vector Machines (SVM) classifier. The DMP is used...
Reliable and fast discrimination between internal faults and inrush conditions is still a challenging issue. In this paper an application of Support Vector Machine (SVM) for the transformer differential protection is discussed. To achieve the satisfactory classification strength various input vectors and training parameters were considered. Finally, 16 different versions of SVM classifiers are proposed...
In this paper, we develop an automatic method for counting palm trees in UAV images. First we extract a set of keypoints using the Scale Invariant Feature Transform (SIFT). Then, we analyze these keypoints with an Extreme Learning Machine (ELM) classifier a priori trained on a set of palm and no-palm keypoints. As output, the ELM classifier will mark each detected palm tree by several keypoints. Then,...
Recognition of complex dynamic texture is a challenging problem and captures the attention of the computer vision community for several decades. Essentially the dynamic texture recognition is a multi-class classification problem that has become a real challenge for computer vision and machine learning techniques. In this paper, we propose a new approach to tackle the dynamic texture recognition problem...
In the aging society such as Japan, it is very important to improve the quality of nursing-care for keeping our quality of life. Our final goal is to develop a computer aided evaluation system to improve the quality of nursing-care. For evaluating the quality of actual nursing, we have been collecting texts that are written by nurses using our Web based system. In our previous works, a SVM based classification...
This paper involves giving a better solution in optimizing Support Vector Machine in classifying sentiments towards a product brand. Sentiment analysis rose to solve the problem of classifying sentiments and classifying as to positive or negative feedback towards a certain product brands. Using the Support Vector Machine learning algorithm, this study aims to improve the algorithm's accuracy through...
Based on the recent success of Low-Rank matrix Representation (LRR), we propose a novel classification method for robust face recognition, named LRR-based Classification (LRRC). By the ideal that if each data class is linearly spanned by a subspace of unknown dimensions and the data are noiseless, the lowest-rank representations of a set of test vector samples with respect to a set of training vector...
Information granules emerging as a result of an abstract and more condensed and global view at numeric data play an essential role in various pattern recognition pursuits. In this study, we investigate an idea of granular prototypes (representatives) and discuss their role in the realization of classification schemes. A two-stage procedure of a formation of information granules is discussed. We show...
A new text classification algorithm has been put forward based on basic support vector machine algorithm. The SVM-KNN algorithm for text classification has been proposed which combined SVM algorithm and KNN algorithm. The SVM-KNN algorithm can improve the performance of classifier by the feedback and improvement of classifying prediction probability. The actual effect of SVM-KNN algorithm is tested...
In recent years, RESTful Web services have been rapidly developed and deployed, because of the advantages of lightweight, flexibility and extensibility, etc. However, most RESTful services are described in heterogeneous and ordinary HTML pages, which makes them really difficult to be identified and crawled automatically from the Internet. In this paper we propose a hybrid classifier framework called...
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