The phrase "approaches from machine" is not correct in standard written English.
It may be intended to describe a method or perspective that originates from a machine, but it lacks clarity and proper grammatical structure.
Example: "The analysis approaches from machine learning algorithms, providing insights that traditional methods cannot."
Alternatives: "comes from machine" or "originates from machine."
Using a regression approach from machine learning, we showed that these features contain significant information which can be used for skin thickness calculation.
In particular, feed-forward neural networks (FFNNs), currently recognized as state-of-the-art approach for statistical prediction of air quality, are compared with two alternative approaches derived from machine learning: pruned neural networks (PNNs) and lazy learning (LL).
In fact, approaches drawing from machine translation have shown considerable skill at such summarization.
In recent years, approaches from the machine-learning community have received more attention for this.
In contrast to these traditional methods, which are based on hand-engineered transformations, the problem can also be approached from a machine learning point of view if representative examples of the task are provided.
This approach uses methods drawn from machine learning, in particular kernels for structured data.
This technical problem was alleviated by algorithms that were developed in the late 2000s, which aggressively incorporated dimensionality reduction approaches from the field of machine learning.
The patterns of activation across these larger regions can be "learned" through approaches from the field of machine learning.
However new approaches from the field of machine learning are increasingly being applied to neuroimaging data with an ability to classify a patient in terms of diagnosis based on their MRI data alone.
The rest of this article is organized as follows: In the next section, after briefly reviewing some multiple-kernel approaches from the statistics and machine-learning literature, we introduce our model that is more suitable to use in the context of traditional SPMMs.
