Feed-forward Neural Networks (FNN) and Support Vector Machines (SVM) are two machine learning frameworks developed from very di:erent starting points of view. In this work a new learning model for FNN is proposed such that, in the linearly separable case, it tends to obtain the same solution as SVM. The key idea of the model is a weighting of the sum-of-squares error function, which is inspired...

A heuristic optimal discrete bit allocation algorithm is proposed for solving the margin maximization problem in discrete multitone (DMT) systems. Starting from an initial equal power assignment bit distribution, the proposed algorithm employs a multistaged bit rate allocation scheme to meet the target rate. If the total bit rate is far from the target rate, a multiple-bits loading procedure is...

In the light of general question posed in title, we write down a very simple randomized learning algorithm, based on boosting, that can be seen as nonstationary Markov random process. Surprisingly, decision hyperplanes resulting from this algorithm converge probability to exact hard-margin solutions support vector machines (SVMs). This fact is curious because hyperplane not statistical solution...

This study was conducted to find the impact of capital structure and liquidity condition on profitability pharmaceutical firms listed with Pakistan Stock Exchange (PSX). The dataset comprised eleven years 2010 2021. To assess level, dimensions return assets (ROA) gross profit margin (GPM) were used. measured through debt equity ratio (DER) total funds (DTF). level current (CR) acid test (ATR). ...

We evaluate the goal of maximizing number individuals matched to acceptable outcomes. present two mechanisms that maximize assignments. The first is Pareto efficient and undominated—in terms assignments—in equilibrium. second fair for unassigned agents assigns weakly more than any stable mechanism in

Bipartite ranking is a fundamental machine learning and data mining problem. It commonly concerns the maximization of the AUC metric. Recently, a number of studies have proposed online bipartite ranking algorithms to learn from massive streams of class-imbalanced data. These methods suggest both linear and kernel-based bipartite ranking algorithms based on first and second-order online learning...

The widespread habit of “plugging” arbitrary symmetric functions as kernels in support vector machines (SVMs) often yields good empirical classification results. However, in case of non conditionally positive definite (non-cpd) functions they are hard to interpret due to missing geometrical and theoretical understanding. In this paper we provide a step towards comprehension of SVM classifiers i...

Recent applications and developments based on support vector machines (SVMs) have shown that using multiple kernels instead of a single one can enhance classifier performance. However, there are few reports on performance of the kernel‐based Fisher discriminant analysis (kernel‐based FDA) method with multiple kernels. This paper proposes a multiple kernel construction ...