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.
Missing values are a common problem in real world data and are particularly prevalent in biomedical time series, where a patient's medical record may be split across multiple institutions or a device may briefly fail. These data are not missing completely at random, so ignoring the missing values can lead to bias and error during data mining. However, current methods for imputing missing values have...
We propose a novel machine learning model for classification problems, Deep Twin Support Vector Machine (DTWSVM), which combines TWSVM with deep learning ideas. TWSVM is a successful algorithm for classification problems which seeks two nonparallel hyper planes to make each hyper plane close to one class and far from the other as much as possible. And Deep Learning (DL) models have shown good ability...
Human face recognition technology is one of the hottest research in the field of pattern recognition at present. In this paper, the principle component analysis (PCA) and bidirectional principle component analysis (BDPCA) methods are proposed to recognize a grayscale face image, for which the size of the spatial distribution is 64 × 64. At first, the main part of the face is extracted to form the...
In this paper, we present the ATM (Awesome Translation Machine), which translates handwriting texts in English into Chinese, and then provides its pronunciations in both the two languages. Specifically, two types of the databases that contain characters and sentences for training the ATM are constructed. Various signal processing techniques are employed sequentially for processing and analyzing the...
In this paper, we propose a two-step approach for the super-resolution reconstruction of video sequences based on the degraded model. Firstly we use the sparse principal component analysis and the linear minimum mean square-error estimation method to remove the noises from the degraded video sequences. Secondly we adopt the Newton-Thiele's vector valued rational interpolation which is one of the nonlinear...
The neural networks are well known as that they have an ability of approximation of any nonlinear function, and they are applied for data prediction in many fields. The parameters of neural networks, the thresholds and the weights between nodes, are updated by using given data. The performance of a neural network, for example prediction accuracy, is evaluated by the degree of the amount of the prediction...
Voting Advice Applications (VAAs) are online tools that match the policy preferences of voters' with the policy positions of political parties or candidates. A recent, innovative extension of VAAs has been to draw on the field of computer science to introduce a social vote recommendation borrowing the basic principles of collaborative filtering. The latter takes advantage of the community of VAA users...
In this paper we extend our previous work on strategies for automatically constructing noise resilient SVM detectors from click through data for large scale concept-based image retrieval. First, search log data is used in conjunction with Information Retrieval (IR) models to score images with respect to each concept. The IR models evaluated in this work include Vector Space Models (VSM), BM25 and...
Principal component analysis (PCA) is an effective statistical technique for face recognition because it can reduce the dimensions of a given unlabeled high-dimensional dataset while keeping its spatial characteristics as much as possible. However, since PCA only explains the covariance structure of all the data its most expressive components, it cannot represent the most important discriminant directions...
Human action recognition from video input has seen much interest over the last decade. In recent years, the trend is clearly towards action recognition in real-world, unconstrained conditions (i.e. not acted) with an ever growing number of action classes. Much of the work so far has used single frames or sequences of frames where each frame was treated individually. This paper investigates the contribution...
Text classification is one of the most significant contents in Natural Language Processing research field. In most real cases, text classification is usually a multi-label learning task. Currently, there are three mainstream attribute measures (i.e., information gain, document frequency and chi-square test values) which are often used to describe documents. The three attribute measures have been applied...
This paper extracts statistical features using a novel approach. The feature set locally measure the characteristics of the image. The proposed approach encodes the extracted features, from a one-pixel width window that slides horizontally the word image. We then inject the feature vector set into a recognition engine. The recognition engine is built using Hidden Markov Models Tool Kit (HTK). The...
Face recognition, one of the most explored themes in biometry, is used in a wide range of applications: access control, forensic detection, surveillance and monitoring system, robotic and human machine interaction. In this paper, a new classifier is proposed for face recognition. The performance of this new classifier is compared with the performance of the KNN classifier. The face image database...
Neuromorphic computing system (NCS) is a promising architecture to combat the well-known memory bottleneck in Von Neumann architecture. The recent breakthrough on memristor devices made an important step toward realizing a low-power, small-footprint NCS on-a-chip. However, the currently low manufacturing reliability of nano-devices and the voltage IR-drop along metal wires and memristors arrays severely...
Indirect immunofluorescence (IIF) with HEp-2 cells is considered as a powerful, sensitive and comprehensive technique for analyzing antinuclear autoantibodies (ANAs). Fractal dimension can be used on the analysis of image representing and also on the property quantification like texture complexity and spatial occupation. In this study, we apply the fractal theory in the application of HEp-2 cell staining...
In this paper we implement state of the art factor analysis based methods and fused their scores to gain a channel robust speaker recognition system. These two methods are joint factor analysis (JFA) and i-Vector which define low-dimensional speaker and channel dependent spaces. For score fusion we propose a simple weight computation without training step. We experiment our method on two conditions;...
Fingerfrinting based WiFi positioning approach needs an off-line training phase to build a radio map with received signal strength indication vector of each reference point. In existing systems, this training phase may cost a tremendous amount of workload to achieve satisfying location result. To cut down on the workload notably and guarantee the location result in the meantime, we will introduce...
Limited size of object images and lack amount of training data would degrade the performance seriously in modern-day recognition applications. Therefore, how to effectively utilize available information from images becomes more and more important. In this paper, we propose to extend the linear regression classification (GLRC), which can effectively use all the information in cases of multiple inputs,...
This paper focuses on the study of modified constructive training algorithm for Multi Layer Perceptron “MLP” which is applied to face recognition applications. In general, constructive learning begins with a minimal structure, and increases the network by adding hidden neurons until a satisfactory solution is found. The contribution of this paper is to increment the output neurons simultaneously with...
There is a great interest in the Genetic Programming (GP) community to develop semantic genetic operators. Most recent approaches includes the genetic programming framework for symbolic regression called Error Space Alignment GP, the geometric semantic operators, and our previous work the semantic crossover based on the partial derivative error. To the best of our knowledge, there has not been a semantic...
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.