Sentence examples for multidimensional training from inspiring English sources

The resort also is home to the Sea Island Golf Learning Center, a multidimensional training program that features some of Golf Digest's top 50 instructors, who work with Love and the other touring pros.

Baker et al. [ 20] went into that matter with a systematic review on randomised controlled trials (RCT) of multidimensional training interventions with people aged 60 years or older.

Crucial points were the lecturer´s expertise, fun factor, subjective appraisal of the achieved anatomical knowledge, as well as evaluation of the additional multidimensional training through ultrasound and arthroscopy.

Multidimensional plots of the training, validation 1 and validation 2 datasets, before and after correction of batch effect show that before application of the batch adjustment algorithm, each dataset clearly separated from all the others ("batch effect"), whereas after correction of batch effect, samples from all datasets were well intermixed (Figure S1).

The authors concluded that resistance training improves multidimensional function, pain, tenderness, and muscle strength in women with FM.

These multidimensional classifiers can be trained and the performance can be characterized in the same way as in the case of the one-dimensional classifier.

This paper addresses the problem of classifying multidimensional data with relatively few training samples available.

Multidimensional concepts, which combine physical training with cognitive-behavioral components in a bio-psycho-social approach, are appreciated as especially promising [ 76].

This presentation format in the curriculum may erroneously suggest to students that empathy is an exclusively inner characteristic or a natural disposition [ 25] rather than a multidimensional competence to be taught, trained, and assessed [ 24, 26].

First, a global AD is determined using a leverage approach that checks whether the query structure falls within the multidimensional chemical space of the whole training set [63].

Compared with prior studies, we not only focused on features extraction but also extended our work to four aspects: accurate pretreatment of the benchmark data, extraction of multidimensional feature vectors [ 18], rebuilding training sets through the oversampling and undersampling approaches, and adoption of a novel ensemble classifier.