Results 91 to 100 of about 27,189 (192)

Robust Linear and Support Vector Regression

, 2000
The robust Huber M-estimator, a differentiable cost function that is quadratic for small errors and linear otherwise, is modeled exactly, in the original primal space of the problem, by an easily solvable simple convex quadratic program for both linear ...
Mangasarian, Olvi, Musicant, David
core  

On Sharp Identification Regions for Regression Under Interval Data [PDF]

, 2013
The reliable analysis of interval data (coarsened data) is one of the most promising applications of imprecise probabilities in statistics. If one refrains from making untestable, and often materially unjustified, strong assumptions on the coarsening ...
Schollmeyer, Georg, Augustin, Thomas
core   +1 more source

A Novel Outlier-Robust Accuracy Measure for Machine Learning Regression Using a Non-Convex Distance Metric

Mathematics
Regression, a supervised machine learning approach, establishes relationships between independent variables and a continuous dependent variable. It is widely applied in areas like price prediction and time series forecasting.
Ahmad B. Hassanat, Mohammad Khaled Alqaralleh, Ahmad S. Tarawneh, Khalid Almohammadi, Maha Alamri, Abdulkareem Alzahrani, Ghada A. Altarawneh, Rania Alhalaseh   +7 more
doaj   +1 more source

Regularization Paths for Generalized Linear Models via Coordinate Descent

Journal of Statistical Software, 2010
We develop fast algorithms for estimation of generalized linear models with convex penalties. The models include linear regression, two-class logistic regression, and multi- nomial regression problems while the penalties include ℓ1 (the lasso), ℓ2 (ridge
Jerome Friedman, Trevor Hastie, Rob Tibshirani   +2 more
doaj  

Machine Learning for Multi Objective Convex Separable Programming (MOCSP) with Aggregation of Linear Approximations and Portfolio Optimization

Foundations of Computing and Decision Sciences
A novel technique is developed for nonlinear optimization problem which is convex, separable and having multiple objective functions. In the development of the model all the objectives and the constraints of the multi objective model are linearly ...
Khan Izaz Ullah, Ahmad Zahoor, Ullah Mehran, Shah Muhammad Shahbaz   +3 more
doaj   +1 more source

Piecewise linear regression via a difference of convex functions [PDF]

, 2020
We present a new piecewise linear regression methodology that utilizes fitting a difference of convex functions (DC functions) to the data. These are functions f that may be represented as the difference _1- _2 for a choice of convex functions _1,_2.
Saligrama, Venkatesh, Gangrade, Aditya, Siahkamari, Ali, Kulis, Brian   +3 more
core  

A Model-Driven Multi-UAV Spectrum Map Fast Fusion Method for Strongly Correlated Data Environments

Drones
Spectrum map fusion has emerged as an effective technique to enhance the accuracy of spectrum map construction. However, many existing fusion methods fail to address the strong correlation between spectrum data, resulting in sub-optimal performance.
Shengwen Wu, Hui Ding, He Li, Zhipeng Lin, Jie Zeng, Qianhao Gao, Weizhi Zhong, Jun Zhou   +7 more
doaj   +1 more source

The Prediction Performance Analysis of the Lasso Model with Convex Non-Convex Sparse Regularization

Algorithms
The incorporation of ℓ1 regularization in Lasso regression plays a crucial role by inducing convexity to the objective function, thereby facilitating its minimization; when compared to non-convex regularization, the utilization of ℓ1 regularization ...
Wei Chen, Qiuyue Liu, Hancong Li, Jian Zou   +3 more
doaj   +1 more source

An Entropic Approach to Constrained Linear Regression

Mathematics
We introduce a novel entropy minimization approach for the solution of constrained linear regression problems. Rather than minimizing the quadratic error, our method minimizes the Fermi–Dirac entropy, with the problem data incorporated as constraints. In
Argimiro Arratia, Henryk Gzyl
doaj   +1 more source

Robust Learning from Bites for Data Mining [PDF]

Some methods from statistical machine learning and from robust statistics have two drawbacks. Firstly, they are computer-intensive such that they can hardly be used for massive data sets, say with millions of data points.
Christmann, Andreas, Steinwart, Ingo, Hubert, Mia   +2 more
core