First-order methods for constrained optimization with new iteration complexity and applications in machine learning

2021 ◽  
Author(s):  
Runchao Ma
Keyword(s):  
Machine Learning ◽  
Constrained Optimization ◽  
Iteration Complexity ◽  
First Order ◽  
First Order Methods

Assessing Medical Evidence

2018 ◽  
Author(s):  
Jacob Stegenga
Keyword(s):  
Quality Assessment ◽  
Randomized Trials ◽  
Empirical Studies ◽  
Allocation Concealment ◽  
Assessment Tools ◽  
Medical Evidence ◽  
Empirical Methods ◽  
First Order ◽  
First Order Methods

Medical scientists employ ‘quality assessment tools’ to assess evidence from medical research, especially from randomized trials. These tools are designed to take into account methodological details of studies, including randomization, subject allocation concealment, and other features of studies deemed relevant to minimizing bias. There are dozens of such tools available. They differ widely from each other, and empirical studies show that they have low inter-rater reliability and low inter-tool reliability. This is an instance of a more general problem called here the underdetermination of evidential significance. Disagreements about the quality of evidence can be due to different—but in principle equally good—weightings of the methodological features that constitute quality assessment tools. Thus, the malleability of empirical research in medicine is deep: in addition to the malleability of first-order empirical methods, such as randomized trials, there is malleability in the tools used to evaluate first-order methods.

Glioblastoma and primary central nervous system lymphoma: differentiation using MRI derived first-order texture analysis – a machine learning study

2021 ◽  
pp. 197140092199897
Author(s):  
Sarv Priya ◽  
Caitlin Ward ◽  
Thomas Locke ◽  
Neetu Soni ◽  
Ravishankar Pillenahalli Maheshwarappa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Central Nervous System ◽  
Nervous System ◽  
Diagnostic Performance ◽  
Model Performance ◽  
Area Under The Curve ◽  
Central Nervous System Lymphoma ◽  
First Order ◽  
Single Slice ◽  
Contrast Enhanced

Objectives To evaluate the diagnostic performance of multiple machine learning classifier models derived from first-order histogram texture parameters extracted from T1-weighted contrast-enhanced images in differentiating glioblastoma and primary central nervous system lymphoma. Methods Retrospective study with 97 glioblastoma and 46 primary central nervous system lymphoma patients. Thirty-six different combinations of classifier models and feature selection techniques were evaluated. Five-fold nested cross-validation was performed. Model performance was assessed for whole tumour and largest single slice using receiver operating characteristic curve. Results The cross-validated model performance was relatively similar for the top performing models for both whole tumour and largest single slice (area under the curve 0.909–0.924). However, there was a considerable difference between the worst performing model (logistic regression with full feature set, area under the curve 0.737) and the highest performing model for whole tumour (least absolute shrinkage and selection operator model with correlation filter, area under the curve 0.924). For single slice, the multilayer perceptron model with correlation filter had the highest performance (area under the curve 0.914). No significant difference was seen between the diagnostic performance of the top performing model for both whole tumour and largest single slice. Conclusions T1 contrast-enhanced derived first-order texture analysis can differentiate between glioblastoma and primary central nervous system lymphoma with good diagnostic performance. The machine learning performance can vary significantly depending on the model and feature selection methods. Largest single slice and whole tumour analysis show comparable diagnostic performance.

scholarly journals Radiomics: a new tool to differentiate adrenocortical adenoma from carcinoma

BJS Open ◽  
2021 ◽  
Vol 5 (1) ◽  
Author(s):  
F Torresan ◽  
F Crimì ◽  
F Ceccato ◽  
F Zavan ◽  
M Barbot ◽  
...  
Keyword(s):  
Machine Learning ◽  
Imaging Techniques ◽  
Second Order ◽  
Learning Approach ◽  
Adrenocortical Adenoma ◽  
Unsupervised Machine Learning ◽  
First Order ◽  
Main Challenge ◽  
Machine Learning Approach ◽  
Adrenocortical Tumours

Abstract Background The main challenge in the management of indeterminate incidentally discovered adrenal tumours is to differentiate benign from malignant lesions. In the absence of clear signs of invasion or metastases, imaging techniques do not always precisely define the nature of the mass. The present pilot study aimed to determine whether radiomics may predict malignancy in adrenocortical tumours. Methods CT images in unenhanced, arterial, and venous phases from 19 patients who had undergone resection of adrenocortical tumours and a cohort who had undergone surveillance for at least 5 years for incidentalomas were reviewed. A volume of interest was drawn for each lesion using dedicated software, and, for each phase, first-order (histogram) and second-order (grey-level colour matrix and run-length matrix) radiological features were extracted. Data were revised by an unsupervised machine learning approach using the K-means clustering technique. Results Of operated patients, nine had non-functional adenoma and 10 carcinoma. There were 11 patients in the surveillance group. Two first-order features in unenhanced CT and one in arterial CT, and 14 second-order parameters in unenhanced and venous CT and 10 second-order features in arterial CT, were able to differentiate adrenocortical carcinoma from adenoma (P < 0.050). After excluding two malignant outliers, the unsupervised machine learning approach correctly predicted malignancy in seven of eight adrenocortical carcinomas in all phases. Conclusion Radiomics with CT texture analysis was able to discriminate malignant from benign adrenocortical tumours, even by an unsupervised machine learning approach, in nearly all patients.

scholarly journals A General Framework for Decentralized Optimization With First-Order Methods

2020 ◽  
Vol 108 (11) ◽  
pp. 1869-1889
Author(s):  
Ran Xin ◽  
Shi Pu ◽  
Angelia Nedic ◽  
Usman A. Khan
Keyword(s):  
General Framework ◽  
Decentralized Optimization ◽  
First Order ◽  
First Order Methods

First-order methods of smooth convex optimization with inexact oracle

2013 ◽  
Vol 146 (1-2) ◽  
pp. 37-75 ◽  
Author(s):  
Olivier Devolder ◽  
François Glineur ◽  
Yurii Nesterov
Keyword(s):  
Convex Optimization ◽  
Smooth Convex ◽  
First Order ◽  
Inexact Oracle ◽  
Smooth Convex Optimization ◽  
First Order Methods

scholarly journals Stabilised explicit Adams-type methods

2021 ◽  
pp. 82-98
Author(s):  
Vasily I. Repnikov ◽  
Boris V. Faleichik ◽  
Andrew V. Moisa
Keyword(s):  
Numerical Experiments ◽  
Step Method ◽  
Type Method ◽  
Constrained Optimisation ◽  
First Order ◽  
Error Constant ◽  
Stability Intervals ◽  
Multi Step Methods ◽  
First Order Methods ◽  
Accuracy And Stability

In this work we present explicit Adams-type multi-step methods with extended stability intervals, which are analogous to the stabilised Chebyshev Runge – Kutta methods. It is proved that for any k ≥ 1 there exists an explicit k-step Adams-type method of order one with stability interval of length 2k. The first order methods have remarkably simple expressions for their coefficients and error constant. A damped modification of these methods is derived. In the general case, to construct a k-step method of order p it is necessary to solve a constrained optimisation problem in which the objective function and p constraints are second degree polynomials in k variables. We calculate higher-order methods up to order six numerically and perform some numerical experiments to confirm the accuracy and stability of the methods.

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