Machine learning methods for robust parameter estimationDominik NeumannTommaso MansiArtificial Intelligence for Computational Modeling of the Heart
Classical methods of various new mechanisms were evaluated, including: multiple populations, for example, MPCPSO26, MCPSO25 and CPSO-H/S24; improved learning strategies, for example, CLPSO28 and NPSO27; refined position update, for example, DPSO29; parameter settings, for example, DNSPSO30...
Alloy modelling has a history of machine-learning-like approaches, preceding the tide of data-science-inspired work. The dawn of computational databases has made the integration of analysis, prediction and discovery the key theme in accelerated alloy research. Advances in machine-learning methods and...
Learning Rate:Make a plot with number of iterations on the x-axis. and J(θ) on the y-axis.If J(θ) ever increases, then you probably need to decrease α.It has been proven that if learning rate α is sufficiently small, then J(θ) will decrease on every iteration.To choose α,t...
Setting Estimation Status If a confidence interval is closer thanToleranceto a parameter bound, as defined in the original fit, the function sets the estimation status toconstrained. If all confidence intervals are further away from the parameter bounds thanTolerance, the function sets the status to...
This paper introduces a novel parameter estimation method for the probability tables of Bayesian network classifiers (BNCs), using hierarchical Dirichlet p
statistical parameter estimation, Wiener and Kalman filtering, convexity and convex optimization, including a chapter on stochastic approximation and the gradient descent family of algorithms, presenting related online learning techniques as well as concepts and algorithmic versions for distributed optimization....
Bootstrap Aggregation (bagging) is a ensembling method that attempts to resolve overfitting for classification or regression problems. Bagging aims to improve the accuracy and performance of machine learning algorithms. It does this by taking random subsets of an original dataset, with replacement, and...
Impact of Business Cycles on Machine Learning Predictions 15.April 2024 As an old investing adage goes, “Everybody’s a genius in a bull market.” It is easy to fall victim to the Dunning-Kruger effect, where attribution bias makes us mistake our luck for abilities. When the business cyc...
When the underlying parameter space of a machine learning model has a certain structure and cannot be best captured by the Euclidean geometry, we rely on computations of the natural gradients and information distances between parameter points to design efficient learning algorithms. Such distances and ...