Search results for "Weighted"
showing 10 items of 190 documents
Bagging and Boosting with Dynamic Integration of Classifiers
2000
One approach in classification tasks is to use machine learning techniques to derive classifiers using learning instances. The co-operation of several base classifiers as a decision committee has succeeded to reduce classification error. The main current decision committee learning approaches boosting and bagging use resampling with the training set and they can be used with different machine learning techniques which derive base classifiers. Boosting uses a kind of weighted voting and bagging uses equal weight voting as a combining method. Both do not take into account the local aspects that the base classifiers may have inside the problem space. We have proposed a dynamic integration tech…
Accelerated T2-Weighted TSE Imaging of the Prostate Using Deep Learning Image Reconstruction: A Prospective Comparison with Standard T2-Weighted TSE …
2021
Multiparametric MRI (mpMRI) of the prostate has become the standard of care in prostate cancer evaluation. Recently, deep learning image reconstruction (DLR) methods have been introduced with promising results regarding scan acceleration. Therefore, the aim of this study was to investigate the impact of deep learning image reconstruction (DLR) in a shortened acquisition process of T2-weighted TSE imaging, regarding the image quality and diagnostic confidence, as well as PI-RADS and T2 scoring, as compared to standard T2 TSE imaging. Sixty patients undergoing 3T mpMRI for the evaluation of prostate cancer were prospectively enrolled in this institutional review board-approved study between O…
Inferring fish abundance in lakes from subfossil midge (Diptera:Chironomidae) remains
2017
This study assessed the possibility of using subfossil remains of chironomid head capsules to reconstruct past fish abundances in lakes. Using data from 20 Swedish lakes, a weighted average (WA) model was created to predict the abundance of benthivorous fish (cyprinids and perch). Chironomid head capsules, identified to genus, served as the predictors to model already recorded (catch per unit effort) fish data. The relationship of chironomid species composition to lake morphometry, water chemistry and fish abundance were examined using ordination and variation partitioning methods. Moreover, the relationship between fish abundance and two chironomid species traits: body size and activity mo…
A Similarity Evaluation Technique for Cooperative Problem Solving with a Group of Agents
1999
Evaluation of distance or similarity is very important in cooperative problem solving with a group of agents. Distance between problems is used by agents to recognize nearest solved problems for a new problem, distance between solutions is necessary to compare and evaluate the solutions made by different agents, and distance between agents is useful to evaluate weights of the agents to be able to integrate them by weighted voting. The goal of this paper is to develop a similarity evaluation technique to be used for cooperative problem solving with a group of agents. Virtual training environment used for this goal is represented by predicates that define relationships within three sets: prob…
Extension of luminance component based demosaicking algorithm to 4- and 5-band multispectral images
2021
Abstract Multispectral imaging systems are currently expanding with a variety of multispectral demosaicking algorithms. But these algorithms have limitations due to the remarkable presence of artifacts in the reconstructed image. In this paper, we propose a powerful multispectral image demosaicking method that focuses on the G band and luminance component. We've first identified a relevant 4-and 5-band multispectral filter array (MSFA) with the dominant G band and then proposed an algorithm that consistently estimates the missing G values and other missing components using a convolution operator and a weighted bilinear interpolation algorithm based on the luminance component. Using the cons…
Integer Weighted Regression Tsetlin Machines
2020
The Regression Tsetlin Machine (RTM) addresses the lack of interpretability impeding state-of-the-art nonlinear regression models. It does this by using conjunctive clauses in propositional logic to capture the underlying non-linear frequent patterns in the data. These, in turn, are combined into a continuous output through summation, akin to a linear regression function, however, with non-linear components and binary weights. However, the resolution of the RTM output is proportional to the number of clauses employed. This means that computation cost increases with resolution. To address this problem, we here introduce integer weighted RTM clauses. Our integer weighted clause is a compact r…
<title>Dynamic integration of multiple data mining techniques in a knowledge discovery management system</title>
1999
One of the most important directions in improvement of data mining and knowledge discovery, is the integration of multiple classification techniques of an ensemble of classifiers. An integration technique should be able to estimate and select the most appropriate component classifiers from the ensemble. We present two variations of an advanced dynamic integration technique with two distance metrics. The technique is one variation of the stacked generalization method, with an assumption that each of the component classifiers is the best one, inside a certain sub area of the entire domain area. Our technique includes two phases: the learning phase and the application phase. During the learnin…
Handling local concept drift with dynamic integration of classifiers : domain of antibiotic resistance in nosocomial infections
2006
In the real world concepts and data distributions are often not stable but change with time. This problem, known as concept drift, complicates the task of learning a model from data and requires special approaches, different from commonly used techniques, which treat arriving instances as equally important contributors to the target concept. Among the most popular and effective approaches to handle concept drift is ensemble learning, where a set of models built over different time periods is maintained and the best model is selected or the predictions of models are combined. In this paper we consider the use of an ensemble integration technique that helps to better handle concept drift at t…
Mean ergodicity of weighted composition operators on spaces of holomorphic functions
2016
[EN] Let phi be a self-map of the unit disc D of the complex plane C and let psi be a holomorphic function on D. We investigate the mean ergodicity and power boundedness of the weighted composition operator C-phi,C-psi(f) = psi(f o phi) with symbol phi and multiplier psi on the space H(D). We obtain necessary and sufficient conditions on the symbol phi and on the multiplier psi which characterize when the weighted composition operator is power bounded and (uniformly) mean ergodic. One necessary condition is that the symbol phi has a fixed point in D. If phi is not a rational rotation, the sufficient conditions are related to the modulus of the multiplier on the fixed point of phi. Some of o…
A three-dimensional study of the onset of convection in a horizontal, rectangular porous channel heated from below
2012
Author's version of an article published in the journal: International Journal of Thermal Sciences. Also available from the publisher at: http://dx.doi.org/10.1016/j.ijthermalsci.2011.12.012 The onset of convection is studied in a rectangular channel filled with a fluid saturated porous medium, bounded above and below by impermeable isothermal walls at unequal temperatures and laterally by partially conducting walls. A three-dimensional linear stability analysis is carried out under the assumption of an infinite longitudinal channel length. Then, this assumption is relaxed in order to determine the threshold length for the three-dimensional convection to be the preferred mode at onset. Sens…