Logistic Regression as Soft Perceptron Learning
We comment on the fact that gradient ascent for logistic regression has a connection with the perceptron learning algorithm. Logistic learning is the "soft" variant of perceptron learning.
Code (0)
등록된 구현이 없습니다.
Tasks
regressionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Logitron: Perceptron-augmented classification model based on an extended logistic loss function
Classification is the most important process in data analysis. However, due to the inherent non-convex and non-smooth structure of the zero-one loss function of the classification model, various convex surrogate loss fun…
ClassificationGeneral ClassificationregressionFrom Logistic Regression to the Perceptron Algorithm: Exploring Gradient Descent with Large Step Sizes
We focus on the classification problem with a separable dataset, one of the most important and classical problems from machine learning. The standard approach to this task is logistic regression with gradient descent (LR…
On Expected Accuracy
We empirically investigate the (negative) expected accuracy as an alternative loss function to cross entropy (negative log likelihood) for classification tasks. Coupled with softmax activation, it has small derivatives o…
ClassificationGeneral Classificationimage-classificationImage Classification+1Customers Churn Prediction in Financial Institution Using Artificial Neural Network
In this study, a predictive model using Multi-layer Perceptron of Artificial Neural Network architecture was developed to predict customer churn in a financial institution. Previous researches have used supervised machin…
Feature Engineeringfeature selectionL2 RegularizationMultinomial Logistic Regression Algorithms via Quadratic Gradient
Multinomial logistic regression, also known by other names such as multiclass logistic regression and softmax regression, is a fundamental classification method that generalizes binary logistic regression to multiclass p…
regression