Sentence examples for method that learns from inspiring English sources

Huang et al. [14] present Supervised Local Subspace Learning (SL2), a method that learns a local linear model from a sparse and non-uniformly sampled training set.

Here, we address a coarse-to-fine strategy-based hand segmentation method that learns appearance feature of hand and background on-the-fly.

We present here a method that learns an evolutionary model from unsupervised data and can also incorporate the labels for supervised evolutionary prediction.

Procedures defined here extend these capabilities by means of a mapping method that learns to identify and distribute arbitrarily many target classes.

Technically speaking, PLCG is an ensemble learning method that learns multiple models via some standard rule learning algorithm, and then combines these into one final rule set via clustering, generalization, and heuristic rule selection.

Binary relevance (BR) is a popular problem transformation method that learns M binary classifiers, one for each different label in L. It transforms the original dataset into M datasets D λ j : j = 1 … M that contain all examples of the original dataset, labeled positively if the label set of the original example contained λ j and negatively otherwise.

Methods that learn models from training data were introduced to overcome these limitations.

In order to provide answers to these questions, we propose mining methods that learn evolutionary predictive models over the evolving cohort of patients.

The most commonly applied VS methods are molecular docking, pharmacophore identification and ligand similarity (including shape based), along with a variety of machine learning methods that "learn" to differentiate actives from inactives based on known data [18, 19].

Methods that learn regulatory associations based on unit-less metrics, such as Mutual Information, are attractive in that they scale well and reduce the number of free parameters (model complexity) per interaction to a minimum.

These results suggest novel research lines able to combine network integration methods, that learn from the data how to weight multiple sources of evidence, with network-based ranking algorithms that can learn from the overall topology of the integrated network how to prioritize candidate disease genes.