In a typical machine learning setting, we have a set of examples E that are described by a set of attributes A, and from these examples we try to induce or learn a model that would generalize the examples.
For example, we tried and returned a Shark vacuum before the Dyson and found it severely lacking.
For example, we tried to represent the concept "day" (meaning a complete 24-hour cycle) by combining a rooster crowing, a clock ticking, and crickets chirping into one sequence.
For example, we tried to decrease stigma and discrimination by following FSWs and non-FSWs together at the same clinic.
For example, we tried by knockdown of miR-101 to rescue the expression levels of Ezh2 in UVB irradiated cells.
For example, we tried throughout the synthesis to distinguish between participants (e.g. boys and girls) where the primary research had made an appropriate distinction.
For example, we tried to target digital native users [ 27] constantly active on SNS as well as light users whose (Facebook®) profiles were nearly empty.
For example, we tried fitting low order polynomials to division rates over time, but the fits were unsatisfactory, as judged by assuming counts were Poisson distributed with rates predicted from the polynomial fit.
For example, we tried to correlate the POD thermal stability to the proline number and position in β-turns [ 54] and to the effect of heme-apoenzyme bonds [ 55].
As an example, we try adding a sill-like deformation source beneath Mt. Aso, proposed by a receiver function study (Abe et al. 2010), to the base model.
With the example, we try to illustrate the applicability of our results and techniques and show that a bounding pair according to Definitions 2.1 or 4.1 can exist naturally.
