While we are excited about what we might achieve, our expectations are tempered by the caveat that deep learning generally requires vastly more
data than traditional approaches to machine learning.
Not exact matches
Their unsupervised
approach performed better
than the more
traditional methods of classification — those based on a set of predetermined features — and managed to reach up to 85.4 % accuracy in the large Brazilian
data set, they report today in PeerJ.
When applied to 100 of the most sought - after fine wines from the Liv - ex 100 wine index, the new
approach predicted prices with greater accuracy
than other more
traditional methods by learning which information was important amongst the
data.
These
data indicate that under many atmospherically - relevant conditions SOA particles are significantly more viscous and orders of magnitude less volatile
than assumed in
traditional modeling
approaches; SOA formation yields can be significantly higher
than previously reported values; and anthropogenic pollution enhance loadings of SOA from biogenic precursors.
When educators learn about the work of Envision Schools and the work of our partner schools in the Deeper Learning Network, they almost always ask for evidence or
data to show that this
approach is better
than traditional approaches to learning.
Arnott has back - tested his methods on historical
data and claims that his fundamental indexing
approach would have outperformed
traditional indexes by more
than two percentage points a year over the past few decades.
[1] Where feral cats are concerned, however, the «Proposed Action» is nothing more
than the «
traditional» trap - and - kill
approach — this, despite the fact that FWS lacks sufficient
data concerning the distribution of, and extent of predation by, feral cats.
Calibration and tuning of coupled Human — Earth System models, as indicated above, could take advantage of optimal parameter estimation using advanced
Data Assimilation, rather
than following the more
traditional approach of tuning individual parameters or estimating them from available observations.
Understanding the
data can offer insights and allow for more effective strategy development earlier in a matter
than traditional approaches.