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.
We explore the use of synthetic benchmarks for the training phase of machine-learning-based automatic performance tuning. We focus on the problem of predicting if the use of local memory on a GPU is beneficial for caching a single target array in a GPU kernel. We show that the use of only 13 real benchmarks leads to poor prediction accuracy (about to 58%) of the 13 leave-one-out models trained using...
Eigenface is one of the most common appearance based approaches for face recognition. Eigenfaces are the principal components which represent the training faces. Using Principal Component Analysis, each face is represented by very few parameters called weight vectors or feature vectors. While this makes testing process easy, it also includes cumbersome process of generating eigenspace and projecting...
To solve the nonlinear response of semiconductor gas sensor and cross-sensitivity to the non-target gases, this paper studies gas sensor array and least square support vector regression (LS-SVR) based gas concentration measurement method. Methane (CH4), hydrogen (H2) and their mixtures are selected as the target gases. A multi-sensor array is composed of four metal oxide semiconductor (MOS) gas sensors...
Modern compilers use machine learning to find from their prior experience useful heuristics for new programs encountered in order to accelerate the optimization process. However, prior experience might not be applicable for outlier programs with unfamiliar code features. This paper presents a Reverse K-nearest neighbor (RKNN) algorithm based approach for outlier detection. The compiler can therefore...
With the increased proliferation of array antennas, for a myriad of wireless applications, the monitoring and correction of array element failure is becoming more critical. In this paper, a fast Support Vector Machine (SVM) classifier, based on low-degree polynomial kernel is developed, to expeditiously detect and locate the failed elements of an arbitrary analog array, while categorizing the level...
Most Chinese text classification methods are based on Chinese word segmentation and bag of words (BOW). The classification performance largely relies on the accuracy of segmentation. Unfortunately, perfect precision and disambiguation of segmentation cannot be reached. In order to solve this problem, a novel Chinese text classification method using string kernel is presented. String kernel computes...
We propose a versatile framework in which one can employ different machine learning algorithms to successfully distinguish between malware files and clean files, while aiming to minimise the number of false positives. In this paper we present the ideas behind our framework by working firstly with cascade one-sided perceptrons and secondly with cascade kernelized one-sided perceptrons. After having...
The photovoltaic (PV) generators have a nonlinear V-I characteristics and maximum power points which vary with the illumination level and temperature. Using maximum power point tracker (MPPT) with the intermediate converter can increase the system efficiency by matching the PV systems to the loads. This paper presents an irradiation level estimator and a maximum power point tracker based on support...
Podaj zakres dat dla filtrowania wyświetlonych wyników. Możesz podać datę początkową, końcową lub obie daty. Daty możesz wpisać ręcznie lub wybrać za pomocą kalendarza.