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Their solution relies on semi-supervised and active learning algorithms based on Gaussian mixture models.
The proposed approach combines two types of learning: semi-supervised and active.
In the following sections, we describe the semi-supervised and active learning algorithms.
This work proposed an approach for the classification of microalgae using a combination of semi-supervised and active learning algorithms.
As explained on Sect. 1, this work uses an approach based on the combination of two learning types: semi-supervised and active, with the objective to classify microalgae.
This paper presents an approach for an automatic/semi-automatic classification of microalgae based on semi-supervised and active learning algorithms, using Gaussian mixture models.
The problem of classification of microalgae was addressed in the work of Drews, Jr., et al. [9], where Gaussian mixture models were used together with semi-supervised and active learning.
In [18], a semi-supervised approach attempts to enhance the performance of relevance feedback by exploiting unlabeled images, integrating semi-supervised learning and active learning.
We conclude by presenting insights into relevant future works by posing some questions, including defining data sampling criteria, domain adaptation modeling, defining criteria for obtaining useful data abstractions, improving semantic indexing, semi-supervised learning, and active learning.
We conclude by identifying important future areas needing innovation in Deep Learning for Big Data Analytics, including data sampling for generating useful high-level abstractions, domain (data distribution) adaption, defining criteria for extracting good data representations for discriminative and indexing tasks, semi-supervised learning, and active learning.
- Supervised and semi-supervised training – We distinguish between supervised and semi-supervised training.
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