Results 31 to 40 of about 8,137,182 (304)
Comparative Analysis of Machine Learning Algorithms for Heart Disease Prediction [PDF]
ITM Web of Conferences, 2021 In the last few years, cardiovascular diseases have emerged as one of the most common causes of deaths worldwide. The lifestyle changes, eating habits, working cultures etc, has significantly contributed to this alarming issue across the globe including ...
Hasan Ruby
doaj +1 more source
Assessing behavioural changes in ALS: cross-validation of ALS-specific measures [PDF]
, 2017 Objective: The Beaumont Behavioural Inventory (BBI) is a behavioural proxy report for the assessment of behavioural changes in ALS. This tool has been validated against the FrSBe, a non-ALS specific behavioural assessment, and further comparison of the ...
BR Brooks +16 more
core +1 more source
Estimating misclassification error: a closer look at cross-validation based methods
BMC Research Notes, 2012 Background To estimate a classifier’s error in predicting future observations, bootstrap methods have been proposed as reduced-variation alternatives to traditional cross-validation (CV) methods based on sampling without replacement.
Ounpraseuth Songthip +3 more
doaj +1 more source
Electronics, 2021 Selecting a final machine learning (ML) model typically occurs after a process of hyperparameter optimization in which many candidate models with varying structural properties and algorithmic settings are evaluated and compared. Evaluating each candidate
Daniel S. Soper
semanticscholar +1 more source
Machine learning approaches for forecasting inflation: empirical evidence from Sri Lanka
Ruhuna Journal of Science, 2023 The aim of this study is to forecast the inflation rate using supervised machine learning models (SMLM). While SMLMs are widely used in various fields, they have also been widely applied to forecast inflation rates.
W.M.S Bandara, W.A.R. De Mel
doaj +1 more source
Human activity recognition making use of long short-term memory techniques [PDF]
, 2019 The optimisation and validation of a classifiers performance when applied to real world problems is not always effectively shown. In much of the literature describing the application of artificial neural network architectures to Human Activity ...
Shenfield, Alex, Wainwright, Richard
core +1 more source
, 2017 This text is a survey on cross-validation. We define all classical cross-validation procedures, and we study their properties for two different goals: estimating the risk of a given estimator, and selecting the best estimator among a given family. For the risk estimation problem, we compute the bias (which can also be corrected) and the variance of ...
+7 more sources
Pitfalls and Remedies for Cross Validation with Multi-trait Genomic Prediction Methods. [PDF]
, 2019 Incorporating measurements on correlated traits into genomic prediction models can increase prediction accuracy and selection gain. However, multi-trait genomic prediction models are complex and prone to overfitting which may result in a loss of ...
Cheng, Hao, Runcie, Daniel
core +1 more source
ECG Arrhythmia Classification using High Order Spectrum and 2D Graph Fourier Transform
Applied Sciences, 2020 Heart diseases are in the front rank among several kinds of life threats, due to its high incidence and mortality. Regarded as a powerful tool in the diagnosis of the cardiac disorder and arrhythmia detection, analysis of electrocardiogram (ECG) signals ...
Shu Liu +3 more
doaj +1 more source
Local Cross-validation for Spectrum Bandwidth Choice [PDF]
, 2000 We investigate an automatic method of determining a local bandwidth for non-parametric kernel spectral density estimates at a single frequency. This procedure is a modification of a cross-validation technique for global bandwidth choices, avoiding the ...
Velasco, Carlos
core +3 more sources