Particularly, deep model structures at the fine-tuning stage are only different in the last fully connected layer for predicting labels.
In "Multi-Instance Multilabel Learning with Weak-Label for Predicting Protein Function in Electricigens," J.-S.
Since we have assumed (W_0) be a mapping matrix from the original feature space to the multi-label shared feature space for predicting every props, (W_0) should concentrate the information of all the predictors and the bags, therefore, should be reasonably configured as a "non-sparse" matrix.
Wu et al. have applied the state-of-the-art MIML with weak-label learning algorithm MIMLwel for predicting protein functions in two typical real-world electricigens organisms widely used in microbial fuel cells studies.
In this paper, we have applied the state-of-the-art MIML with weak-label learning algorithm MIMLwel for predicting protein functions in two typical real-world electricigens organisms which have been widely used in microbial fuel cells (MFCs) researches.
On the other hand, labeled text data is often more available than labeled image data and text features have more semantic meaning for predicting a class label.
The learned clustering parameters, MinPts and ε, from recordings made with Mic1 are used for predicting cluster labels for recordings made using Mic2 and Mic3.
In the next section, we first describe learning of transition model and then explain how it is used for predicting the labels of the individuals.
To tackle these problems, this paper proposes an interpretable multi-label predictor, namely Gram-LocEN, for predicting the subcellular localization of both single- and multi-location proteins of Gram-positive or Gram negative bacteria.
In this paper, we first propose GM-SMCC, an effective and novel semi-supervised multi-label collective classification based method for predicting functional properties of proteins.
We conclude our work in Section 7. Classification aims to build an efficient and effective model for predicting class labels of unknown data.
