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- W2492879181 abstract "Regardless of which data analysis method is chosen to build a prediction model, the training and structure of the model should lead to reliable and well performing prediction models even for unknown video sequences not included in the training. In this chapter, considerations in the model building process are addressed that should be taken into account when using the data analysis approach in the design of video quality metrics. Firstly, the training of reliable prediction models using cross validation is explained, especially the advantages and disadvantages of simple cross validation, leave-one-out cross validation and k-fold cross validation. This is complemented by a discussion of the parsimony principle in selecting a specific model from among multiple candidate models, followed by a discussion of influencing the structure of the model and the resulting predication performance by selectively choosing the number of components or specific features." @default.
- W2492879181 created "2016-08-23" @default.
- W2492879181 creator A5074400591 @default.
- W2492879181 date "2015-12-30" @default.
- W2492879181 modified "2023-09-25" @default.
- W2492879181 title "Model Building Considerations" @default.
- W2492879181 doi "https://doi.org/10.1007/978-981-10-0269-4_7" @default.
- W2492879181 hasPublicationYear "2015" @default.
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