Abstract: The question of polynomial learn ability of probability distributions, particularly Gaussian mixture distributions, has recently received significant attention in theoretical computer ...

Abstract: In pattern classification, polynomial classifiers are well-studied methods as they are capable of generating complex decision surfaces. Unfortunately, the use of multivariate polynomials is ...

Project Description: Use machine learning for predicting statistics or properties of polynomials. The main goal is to find appropriate encodings of polynomials and answer questions such as the number ...

We introduce a novel AI-driven approach to unsupervised fundus image registration utilizing our Generalized Polynomial Transformation (GPT) model. Through the GPT, we establish a foundational model ...

Let's plot the results. First create some new x values which are evenly spaced and use the model to predict what the wage would be for these ages. The reason we use I() is because the ^ symbol has a ...

This paper proposes a new learning system of low computational cost, called fast polynomial kernel learning (FPL), based on regularized least squares with polynomial kernel and subsampling. The almost ...

This paper presents an approach to learning polynomial feedforward neural networks (PFNNs). The approach suggests, first, finding the polynomial network structure by means of a population-based search ...