Missing data is a significant problem found in data mining projects and data analysis.
Despite being a common problem, the missing data is dealt in a simplistic way and may lead …

Datasets with missing values are frequent in real-world classification problems. It seems
obvious that imputation of missing values can be considered as a series of secondary tasks …

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 …

2008/0103996 A1 5/2008 Forman et al. 2011/0302118 A1* 12/2011 Melvin GOON 20/00
706/21 2013/0013539 A1 1/2013 Chenthamarakshan et al. 2013/0198119 Al 8/2013 …

In this paper, we discuss machine learning methods for classifying gross kicking activity for
the term and preterm infants. We examine different combinations of sensors to determine the …

The imputation of missing values is an important research content in incomplete data
analysis. Based on the auto associative neural network (AANN), this paper conducts …

Missing input data is a common drawback that pattern classification techniques need to deal
with when solving real-life classification tasks. The ability of dealing with missing data has …

This paper introduces the cosine neural network (COSNN) and shows how it can be used to
process data with missing components without imputation. It uses a cosine basis function …

In this paper, we focus on the quantitative quality of service (QoS) guarantee in a
differentiated services (DiffServ) domain that can be granted to an expedited forwarding (EF) …

In this thesis, temporal and spatial dependence are considered within nonparametric priors
to help infer patterns, clusters or segments in data. In traditional nonparametric mixture …