Statistical analysis with missing data
Statistical analysis with missing data
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Multitask learning
Selective transfer of neural network task knowledge
Selective transfer of neural network task knowledge
Learning Multiple Tasks with Kernel Methods
The Journal of Machine Learning Research
Exploiting multitask learning schemes using private subnetworks
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Classifying patterns with missing values using Multi-Task Learning perceptrons
Expert Systems with Applications: An International Journal
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Incomplete data is a common drawback in many pattern classification applications. A classical way to deal with unknown values is missing data estimation. Most machine learning techniques work well with missing values, but they do not focus the missing data estimation to solve the classification task. This paper presents effective neural network approaches based on Multi-Task Learning (MTL) for pattern classification with missing inputs. These MTL networks are compared with representative procedures used for handling incomplete data on two well-known data sets. The experimental results show the superiority of our approaches with respect to alternative techniques.