Serwis Infona wykorzystuje pliki cookies (ciasteczka). Są to wartości tekstowe, zapamiętywane przez przeglądarkę na urządzeniu użytkownika. Nasz serwis ma dostęp do tych wartości oraz wykorzystuje je do zapamiętania danych dotyczących użytkownika, takich jak np. ustawienia (typu widok ekranu, wybór języka interfejsu), zapamiętanie zalogowania. Korzystanie z serwisu Infona oznacza zgodę na zapis informacji i ich wykorzystanie dla celów korzytania z serwisu. Więcej informacji można znaleźć w Polityce prywatności oraz Regulaminie serwisu. Zamknięcie tego okienka potwierdza zapoznanie się z informacją o plikach cookies, akceptację polityki prywatności i regulaminu oraz sposobu wykorzystywania plików cookies w serwisie. Możesz zmienić ustawienia obsługi cookies w swojej przeglądarce.
An interesting observation about the well-known AdaBoost algorithm is that, though theory suggests it should overfit when applied to noisy data, experiments indicate it often does not do so in practice. In this paper, we study the behavior of AdaBoost on datasets with one-sided uniform class noise using linear classifiers as the base learner. We show analytically that, under some ideal conditions,...
This paper presents a demo proposal of a standalone smartphone application that can automatically analyse the signal quality of PCG, as it is recorded on a low-cost smartphonebased digital stethoscope. Features, related to the inherent pattern of the autocorrelated signal envelope, have been used for classifying and discarding the noisy portions from a continuous PCG. Our application has been successfully...
Deep Neural Networks have become increasingly popular due to their efficient realization in GPU hardware. Problems that were once considered computationally intensive to implement using Neural networks have now become possible due to the vast amount of flexibility and capability offered by the GPU and Deep networks combination. In this work, we attempt to improve the recognition rate for images, using...
There is always noise inside the digital images. Noise is an unwanted component of the image. The existence of noise in a face image can degrade the accuracy of a face recognition. Therefore, we need a proper method that can cope noise or restore the quality of the image. The best method to overcome noise in the image is to use smoothing (filter). In this research, we discuss some techniques to overcome...
This paper presents a novel and unsupervised approach for discovering “sudden” movements in video surveillance videos. The proposed approach automatically detects quick motions in a video, corresponding to any action. A set of possible actions is not required and the proposed method successfully detects potentially alarm-raising actions without training or camera calibration. Moreover, the system...
A problem of detecting textural areas in images corrupted by noise is considered. Detection is based on joint use of several local parameters calculated in scanning windows (blocks) of different size. Trained support vector machine (SVM) classifier is used for combining local parameters. Factors that influence detector performance are analyzed. It is shown that detector performance can be improved...
Trajectories obtained from low level tracking algorithm provide an opportunity for us to analyze meaningful behaviors and monitor adverse or malicious events. How to abstract meaningful features from the raw data of trajectories is a challenge due to the high dimensionality and noise. In this paper, a novel approach, stacked denoising autoencoder(SDA) is applied to address this problem. This method...
In order to improve the accuracy of INS/GPS integrated navigation system during GPS signals blockage, an effective and low-cost method is to design the corresponding linear or non-linear predictor to predict the position and velocity errors between INS and GPS during GPS blockage and then to correct the results of INS. Based on the distributed data fusion system, a novel hybrid prediction method that...
Testing analog and mixed-signal circuits is a costly task due to the required test time targets and high end technical resources. Indirect testing methods partially address these issues providing an efficient solution using easy to measure CUT information. In this work, the pass/fail test regions are encoded using octrees in the measure space. These octrees, generated in the training phase, will serve...
Noise is a prominent challenge found in many bioinformatics datasets and it refers to erroneous or missing data. The presence of noise in gene expression datasets has adverse effects on machine-learning techniques, such as supervised classification algorithms and feature selection techniques. Additionally, the identification of noise and its quantification are challenging tasks that require a proper...
Least squares support vector machine (LS-SVM) has been successfully applied in many classification and regression tasks. The main drawback of the LS-SVM algorithm is the lack of sparseness. Combing the primal least squares twin support vector machine (LS-TSVM) and the sparse LS-SVM with L0-norm minimization, a new sparse least squares support vector regression algorithm with L0-norm in primal space(L...
A method based on sparse denoising autoencoder for denoising hybrid noises in image is proposed in this paper. The method is experimented on natural images and the performance is evaluated in terms of peak signal to noise ratio (PSNR). By specifically designing the training process of sparse denoising autoencoder, our model not only achieves good performance on single kind of noises, but also is relatively...
Speech enhancement and speech separation are important frontends of many speech processing systems. In real tasks, the background noises are often mixed with some human voice interferences. In this paper, we explore a framework to unify speech enhancement and speech separation for a speaker-dependent scenario based on deep neural networks (DNNs). Using a supervised method, DNN is adopted to directly...
We present a boosting method for classification problems with optimal AUC value as a performance measure. The proposed technique first minimizes the empirical pairwise classification error. Once the pairwise classification error is reduced to a coordinatewise local minimum, then it switches to maximize the average pairwise margin of a small set of bottom sample pairs. Experimental results on real-world...
EEG based biometric system can be used for authentication, with advantages like confidentiality retention and forgery prevention. Signals which are taken from maximum brain regions show some sort of unique information that can be used for extracting the subject dependent pattern. This paper presents an approach to find the relationships among signals generated in different brain regions which give...
Data Streams are instances that arrive at a very rapid rate with changes in underlying conceptual distributions. Many ensemble learning approaches were developed to handle these changes in the dataset, which proved to be better than a single classifier system. In our work, we will discuss the framework of our new approach, Double Weighted Methodology and empirically prove it to be better than the...
The recently introduced Dynamic Cortex Memory (DCM) is an extension of the Long Short Term Memory (LSTM) providing a systematic inter-gate connection infrastructure. In this paper the behavior of DCM networks is studied in more detail and their potential in the field of gradient-based sequence learning is investigated. Hereby, DCM networks are analyzed regarding particular key features of neural signal...
We present the first measurement of PMD mitigation in Stokes vector direct detection (SV-DD) systems. By applying two novel algorithms, PMD tolerance has been improved from 3.5 to 9 ps for a 93-Gb/s SV-DD system.
This paper explores the enhancement by locality constraint to both learning and coding schemes, more specifically, discriminative low-rank dictionary learning and auto-encoder. Previous Fisher discriminative based dictionary learning has led to interesting results by learning more discerning sub-dictionaries. Also, the low-rank regularization term has been introduced to take advantage of the global...
In this paper, we present a complete framework for video-based age and gender classification which performs accurately on embedded systems in real-time and under unconstrained conditions. We propose a segmental dimensionality reduction technique using Enhanced Discriminant Analysis (EDA) to reduce the memory requirements up to 99.5%. A non-linear Support Vector Machine (SVM) along with a discriminative...
Podaj zakres dat dla filtrowania wyświetlonych wyników. Możesz podać datę początkową, końcową lub obie daty. Daty możesz wpisać ręcznie lub wybrać za pomocą kalendarza.