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Data augmentation methods for bio-signal classification are proposed. These methods improve recognition performance of human mental states showing intrinsic motivation from brain wave. Conventionally, data augmentation is used to image recognition research. Scaling, rotation, and distortion are applied to the original images to increase examples for machine learning. However, these augmentation methods...
Modelling non-stationary time-series is a challenging but important task. One of the key issues we face is explaining such data with stationary parametric models without creating overly complex models. Identifying trends in time-varying data is a key step in simplifying such models and deconstructing signals into realizations of local models separated by change-points. We study the problem of simultaneous...
As more people embrace online examinations, the need to protect their credibility becomes more crucial. Impersonation is a huge challenge when administering online examinations due to the anonymity of online users. In this paper, we address this problem through the use of keystroke dynamics which refers to the identification of users based on their typing pattern. Our results have supported the fact...
In Korea, authors of the newspaper article tend to express their intention indirectly, that is, they choose a method to leave out some important facts, or sometimes uses biased terms to support their opinion. Since they're not expressing their opinion directly, detecting the political bias is a difficult task. In this paper, we propose a method to detect political bias in the Korean articles by first...
In in vitro histological assessment, mitotic cells play one of the key roles for the diagnosis of oral squamous cell carcinoma (OSCC). In view of this, our paper aims to develop a computer assisted mitotic cell segmentation scheme for automated recognition from microscopic images of OSCC. The methodology includes multilevel thresholding, statistical moment features and classification and regression...
Historical data provide valuable information for the nderstanding of human interactions through time. However, mining this data is challenging as the available records are generally noise digitized handwritten, typewritten or press printed documents. In this research proposal, we plan to develop tools and techniques for pre-processing and extracting information from documents of the military dictatorship...
Classification problems with class imbalance occur when prior probabilities for the data classes differ significantly. The use of one-class classifiers is one of the main approaches to solving such problems. We conduct a comparative study of one-class classification algorithms in classification problems with extreme class imbalance. Emphasis is placed on evaluation of the classificatory accuracy of...
A timely scheduling model is studied and a solution on load balance is attempted to explore from the point of machine learning in this paper. An expert system scheduling algorithm based on Support Vector Machine is presented. After research, the corresponding scheduling model is built, which is applied to the load balance of server cluster. Finally, the feasibility and validity of the algorithm is...
This paper examines the theory and practice of regression techniques for prediction of stock price trend by using a transformed data set in ordinal data format. The original pre-transformed data source contains data of heterogeneous data types used for handling of currency values and financial ratios. The data formats in currency values and financial ratios provide a process for computation of stock...
We study a game-theoretic model of how individuals learn by observing others' acting, and how (causal) knowledge grows in communities as result. We devise a cooperative solution in this game, which motivates a new recommendation system where causality (not correlation) is the central concept. We use the system in low-income communities, where individuals make judgments about the efficiency of educational...
In order to incorporate various writing styles or fonts in a character recognizer, it is critical that a large amount of labeled data is available, which is difficult to obtain. In this work, we present a semi-supervised SVM based framework that can incorporate the unlabeled data for improvement of recognition performance. Existing semi supervised learning methods for SVMs work well only for two-class...
In this paper, we construct a positive definite kernel associated with Slepian semi-wavelets. The kernel possesses multiscale structure and exhibits a strong localization property. It is convolution type associated with asymptotic sparse Gram matrix and allows the use of thresholding methods. We then focus on developing practical numerical algorithm to compute the kernel. Applications of the kernel...
In view of the problems of using traditional machine learning method to detect the network intrusions, a network intrusion detection model based on multi-class imbalanced learning is proposed. Based on the consideration that there is within-class imbalance in large data sets and multi-class data sets, every class of the training data is firstly clustered. Some minimum bounding hyperspheres are formed...
We performed a single pendulum simulation and observed the influence of the situation space segmentation pattern in reinforcement learning processes in order to propose a new adaptive automation for situation space segmentation. Usually, in real-world reinforcement learning processes, infinite states and actions and the uncertainty of the optimum solution make the learning process more difficult than...
This paper makes use of knowledge granular to present a new method to mine rules based on granule. First, use the measure to measure the importance of attribute, and get the granularity of the universe, and then repeat this procedure to every granule of the granularity, until the decision attribute has only one value for all granules, then we will describe every granule to get the rule. The analysis...
Neuron classification is the research basis and also a difficult issue for neuroscience. In this paper, a novel neuronal morphology classification method based on support vector machine (SVM) was proposed. In this method, we first estimated the neuronal geometrical morphological features according to the original space geometric data. Then we utilized SVM to classify the neurons based on the new morphological...
The target classifier is an ingredient of the target recognition system. In order to achieve the automation and computerization of target recognition, a method for training target classifier based on semi-supervised learning is provided. It adopts CFS algorithm for dada feature selection, and uses semi-supervised learning algorithm, Co-training to construct the target classifiers. The final classifier...
Semi-supervised Support Vector Machines is an appealing method for using unlabeled data in classification. Based on a smooth approximation function named as aggregate function, a global aggregate homotopy method is presented in this paper. Compared to some existing algorithms, the new method is superior in no need of introducing extra variables or solving a sequence of subproblems. Moreover, the global...
In this paper, we present an approach to automatically extract and classify opinions in texts. We propose a similarity measurement calculating semantically distances between a word and predefined subgroups of seed words. We have evaluated our algorithm on the semantic evaluation company “SemEval 2007” corpus, and we obtained the best value of Precision and F1 62% and 61%. As an improvement of 20 %...
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