Maximum Likelihood Weights for a Linear Ensemble of Regression Neural Networks
نویسندگان
چکیده
In this we paper study the problem of combining the outputs of the members of an ensemble of neural networks. We review the commonly used methods and thoroughly derive a cost function from a maximum likelihood perspective, that can be minimized in order to obtain maximum likelihood weights. The solution is shown to be closely related to a well known statistical method. The various combination methods are tested on several data-sets, with encouraging results.
منابع مشابه
An empirical analysis of dropout in piecewise linear networks
The recently introduced dropout training criterion for neural networks has been the subject of much attention due to its simplicity and remarkable effectiveness as a regularizer, as well as its interpretation as a training procedure for an exponentially large ensemble of networks that share parameters. In this work we empirically investigate several questions related to the efficacy of dropout,...
متن کاملسودمندی رگرسیون‌های تجمیعی و روش‌های انتخاب متغیرهای پیش‌بین بهینه در پیش‌بینی بازده سهام
مقاله حاضر به بررسی سودمندی رگرسیون‌های تجمیعی و روش‌های انتخاب متغیرهای پیش‌بین بهینه (شامل روش مبتنی بر همبستگی و ریلیف) برای پیش‌بینی بازده سهام شرکت‌های پذیرفته شده در بورس اوراق بهادار تهران می‌پردازد. به‌منظور ارزیابی عملکرد رگرسیون تجمیعی، معیارهای ارزیابی (شامل میانگین قدرمطلق درصد خطا، مجذور مربع میانگین خطا و ضریب تعیین) مربوط به پیش‌بینی این روش، با رگرسیون خطی و شبکه‌های عصبی مصنوعی...
متن کاملMonitoring of Regional Low-Flow Frequency Using Artificial Neural Networks
Ecosystem of arid and semiarid regions of the world, much of the country lies in the sensitive and fragile environment Canvases are that factors in the extinction and destruction are easily destroyed in this paper, artificial neural networks (ANNs) are introduced to obtain improved regional low-flow estimates at ungauged sites. A multilayer perceptron (MLP) network is used to identify the funct...
متن کاملMachine learning algorithms in air quality modeling
Modern studies in the field of environment science and engineering show that deterministic models struggle to capture the relationship between the concentration of atmospheric pollutants and their emission sources. The recent advances in statistical modeling based on machine learning approaches have emerged as solution to tackle these issues. It is a fact that, input variable type largely affec...
متن کاملA Bayesian Nominal Regression Model with Random Effects for Analysing Tehran Labor Force Survey Data
Large survey data are often accompanied by sampling weights that reflect the inequality probabilities for selecting samples in complex sampling. Sampling weights act as an expansion factor that, by scaling the subjects, turns the sample into a representative of the community. The quasi-maximum likelihood method is one of the approaches for considering sampling weights in the frequentist framewo...
متن کامل