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For count type response variable data it deals with over-dispersion by using proper over-dispersed discrete distributions. Heterogeneity also is dealt with by modeling the scale or shape parameters using explanatory variables. There are several packages written in R related to GAMLSS models, [3] and tutorials for using and interpreting GAMLSS. [4]
McCaffrey, J.D., "Introduction to CNTK Succinctly for Syncfusion", [3] An eBook focused on Microsoft CNTK (Cognitive Toolkit, formerly Computational Network Toolkit), an open source code framework that enables you to create deep learning systems, such as feed-forward neural network time series prediction systems and convolutional neural network ...
AIMA gives detailed information about the working of algorithms in AI. The book's chapters span from classical AI topics like searching algorithms and first-order logic, propositional logic and probabilistic reasoning to advanced topics such as multi-agent systems, constraint satisfaction problems, optimization problems, artificial neural networks, deep learning, reinforcement learning, and ...
Python has the statsmodelsS package which includes many models and functions for time series analysis, including ARMA. Formerly part of the scikit-learn library, it is now stand-alone and integrates well with Pandas. PyFlux has a Python-based implementation of ARIMAX models, including Bayesian ARIMAX models.
In most published work on GP, a LISP-style tree-structured expression is directly manipulated, whereas GE applies genetic operators to an integer string, subsequently mapped to a program (or similar) through the use of a grammar, which is typically expressed in Backus–Naur form. One of the benefits of GE is that this mapping simplifies the ...
Transfer learning (TL) is a technique in machine learning (ML) in which knowledge learned from a task is re-used in order to boost performance on a related task. [1] For example, for image classification , knowledge gained while learning to recognize cars could be applied when trying to recognize trucks.
Machine learning in environmental metagenomics can help to answer questions related to the interactions between microbial communities and ecosystems, e.g. the work of Xun et al., in 2021 [50] where the use of different machine learning methods offered insights on the relationship among the soil, microbiome biodiversity, and ecosystem stability.