[PDF] Top 20 Some Regression Models and Algorithms for Functional Data.
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Some Regression Models and Algorithms for Functional Data.
... simple functional linear model, and has also been used in the context of non-parametric regression (Hastie and Tibshirani, 1990; Eilers and Marx, 1996; Goldsmith et ...additive models (Wood, 2006a), ... See full document
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REGRESSION MODELS
... Distribution; Algorithms; Artificial Intelligence; Compilers & Translation; Computer Aided Design (CAD); Computer Aided Manufacturing; Computer Graphics; Computer Organization & Architecture; Database ... See full document
11
Bayesian Graphical Models for Multivariate Functional Data
... transform functional data from the function space to the isometrically isomorphic space of basis coefficients, where Markov distributions and hyper Markov laws can be conveniently ...MCMC algorithms ... See full document
27
Incorporating covariates in skewed functional data models
... feasible algorithms. Models for functional data that accommodate covariate effects have been investigated ...index models to summarize the covariate effect in the mean function, and ... See full document
23
Analysing various Regression Models for Data Processing
... various regression models on data to predict ...test data was taken from vehicle data it contains map velocity which traffic moves, driver velocity and ...how regression was used ... See full document
6
Estimation of functional sparsity in nonparametric varying coefficient models for longitudinal data analysis
... in functional regression is known to be intrinsically difficult ...in functional linear regression with a scalar response and single functional ...truncated regression model with ... See full document
29
Performance Evaluation of Various Classification Algorithms
... the data is categorized into 2 or more ...classification models on the dataset using scikit-learn ...Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, and K-Nearest Neighbor ... See full document
6
Some Greedy Learning Algorithms for Sparse Regression and Classification with Mercer Kernels
... generate models which perform at least as well as SVMs, although the training costs incurred in the former are much lower than those in the latter. Further, the increased sparsity levels(n) shown in the table for ... See full document
21
Genomic Bayesian functional regression models with interactions for predicting wheat grain yield using hyper-spectral image data
... HTPP data analy- ses that take into account not only all the reflectance bands measured in different environments, but also the genomic (and pedigree) information and the interactions between genotype and ... See full document
29
Structured penalties for functional linear models—partially empirical eigenvectors for regression
... with functional data is incorporating ge- ometric structure, or local correlation, into the ...a functional linear model is often used to estimate the relationship between the predictor functions and ... See full document
31
Functional Data Analysis Applied to Modeling of Severe Acute Mucositis and Dysphagia Resulting From Head and Neck Radiation Therapy.
... NTCP models is suboptimal dimensionality reduction of the RT dose distribution (reducing the number of variables used to describe the dose distribution from all of the points on the 3D dose grid to a small number ... See full document
92
A Comparative Study on Data Mining Algorithms for Classification & Regression
... namely, data selection, pre-processing (or) data cleaning, data transformation (or) reduction, data mining task (or) algorithm selection, and finally post processing (or) explanation of ... See full document
12
Flexible Regression Models for Functional Responses.
... physical models for predicting moisture content profiles over time based on predictors that are easily available either from weather forecast ...available data using the proposed function-on-function ... See full document
131
Process Based Online Contents with Offensive Content Detection
... learning models with associated learning algorithms that analyze data used for classification and regression ...When data are not labeled, supervised learning is not possible, and an ... See full document
5
Multivariate Functional Regression for Classification With an Application to Geology
... Using data from the PETROSCOPE project [19], the present investigation deals with classifying 12 different rock types or varieties of the same type with different tex- tural properties, different porosity, and ... See full document
6
A Smart Home Monitoring System for Abnormal Human Activity Detection Using SVM
... learning algorithms that solve the well known clustering ...a data mining which compares existing data of activity with run time old edge peoples activity for finding out abnormal activity and divide ... See full document
15
Applications of some discrete regression models for count data
... the data and their properties we refer Gonzalez ...several models, we found that these variables are statistically significant to predict the number of ...different data sets by deleting zero ROR ... See full document
16
Projecting Health Outcomes for Portuguese Ageing Population: Challenges and Opportunities
... on functional fitness outcomes, enhancing the quality of life of these population, namely within the context of the community physical activity programs that has been recently implemented in the Portuguese society ... See full document
10
Analysis of Zero Inflated Over dispersed Count Data Regression Models with Missing Values
... Discrete data in the form of counts arise in many health science disciplines such as biology and ...count data cases this property of the Poisson distribution does not hold as extra dispersion (variation) ... See full document
132
Machine Learning: Survey, Types and Challenges
... labeled data or information and allows to the algorithm to act on that information without any prior ...given data unsupervised learning is very helpful in exploratory data analysis (EDA) because it ... See full document
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