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Ale mamy wyniki w innych katalogach.Wyniki wyszukiwania dla: ensamble
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Ensemble Classifier for Mining Data Streams
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Agent-Based Data Reduction Using Ensemble Technique
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EBE: elastic blob ensemble for coarse human tracking
PublikacjaProponujemy nowy probabilistyczny algorytm śledzenia oparty na elastycznym zespole kropelkowym (EBE), który ma zastosowanie przy śledzeniu obiektów elastycznych. Wynikiem jest wskazówka nt. zgrubnego ruchu w postaci lokalizacji i orientacji obiektu wraz z lokalizacją kropelki. Głównym założeniem jest to, że orientacja całego obiektu nie zmienia się znacznie między sąsiednimi klatkami. Dyskretna przestrzeń rozwiązań jest tworzona...
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Electrochemistry from first-principles in the grand canonical ensemble
PublikacjaProgress in electrochemical technologies, such as automotive batteries, supercapacitors, and fuel cells, depends greatly on developing improved charged interfaces between electrodes and electrolytes. The rational development of such interfaces can benefit from the atomistic understanding of the materials involved by first-principles quantum mechanical simulations with Density Functional Theory (DFT). However, such simulations are...
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Heterogeneous Ensemble with Combined Dimensionality Reduction for Social Spam Detection
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The detection of Alternaria solani infection on tomatoes using ensemble learning
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SMOTE-Based Homogeneous Ensemble Methods for Software Defect Prediction
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Ensemble-Based Logistic Model Trees for Website Phishing Detection
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Estimation of a Stochastic Burgers' Equation Using an Ensemble Kalman Filter
PublikacjaIn this work, we consider a difficult problem of state estimation of nonlinear stochastic partial differential equations (SPDE) based on uncertain measurements. The presented solution uses the method of lines (MoL), which allows us to discretize a stochastic partial differential equation in a spatial dimension and represent it as a system of coupled continuous-time ordinary stochastic differential equations (SDE). For such a system...
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Divide and not forget: Ensemble of selectively trained experts in Continual Learning
PublikacjaClass-incremental learning is becoming more popular as it helps models widen their applicability while not forgetting what they already know. A trend in this area is to use a mixture-of-expert technique, where different models work together to solve the task. However, the experts are usually trained all at once using whole task data, which makes them all prone to forgetting and increasing computational burden. To address this limitation,...