Accuracy assessment of applied supervised machine learning models on usual data probability distributions

Machine Learning Models for Housing Prices Forecasting using Registration Data

This article has been compiled to identify the best model of housing price forecasting using machine learning methods with maximum accuracy and minimum error. Five important machine learning algorithms are used to predict housing prices, including Nearest Neighbor Regression Algorithm (KNNR), Support Vector Regression Algorithm (SVR), Random Forest Regression Algorithm (RFR), Extreme Gradient B...

Supervised Learning of Probability Distributions by Neural Networks

We propose that the back propagation algorithm for supervised learning can be generalized, put on a satisfactory conceptual footing, and very likely made more efficient by defining the values of the output and input neurons as probabilities and varying the synaptic weights in the gradient direction of the log likelihood, rather than the 'error'. In the past thirty years many researchers have st...

Methods of Data Analysis Learning probability distributions

One of the key problems in non-parametric data analysis is to infer good models of probability distributions, assuming we are given as data a finite sample from that distribution. This problem is ill-posed for continuous distributions, even when they are low-dimensional: with finite data, there is no way to distinguish between (or exclude) distributions that are not regularized a priori, for ex...

Improving Orbit Prediction Accuracy through Supervised Machine Learning

Due to the lack of information such as the space environment condition and resident space objects’ (RSOs’) body characteristics, current orbit predictions that are solely grounded on physics-based models may fail to achieve required accuracy for collision avoidance and have led to satellite collisions already. This paper presents a methodology to predict RSOs’ trajectories with higher accuracy ...

Optimal Data Distributions in Machine Learning