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Sometimes the machine learning literature is daunting because it changes so quickly.
Multi-objective optimization has been extensively studied in the machine learning literature.
This course will cover statistical methods based on the machine learning literature that can be used for causal inference.
Inspired by ideas taken from the machine learning literature, new regularization techniques have been recently introduced in linear system identification.
I will also describe methodological issues that arise that are relatively neglected in the machine learning literature, such as measurement error and the impact of unobserved variables.
The latter are usually called covariates or regressors in the statistical and machine learning literature.
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In the integration process, the K means and fuzzy c means clustering approaches from the pattern recognition and machine learning literatures are employed to integrate non-featured areas for a smooth fused surface and featured areas for keeping the geometric details.
The ability of each type of fingerprint to predict similarity between molecules was determined using logistic regression, receiver operating characteristic (ROC) curves, and a range of measures of predictive success taken from the information retrieval and machine learning literatures.
In the data-mining and machine-learning literature, alternate classification schemes have been developed.
It is common in the machine-learning literature to use features, attributes, dimensions, and metrics interchangeably; here, we will consistently use the term 'features.' In Fig. 1 there is a pictorial representation of the matrix factorization process with two low-rank factors.
Here, we propose an approach for choosing summary statistics based on boosting, a technique from the machine-learning literature.
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