Time series classification is an increasing research topic due to the vast amount of time
series data that is being created over a wide variety of fields. The particularity of the data …

This paper provides a brief survey of the basic concepts and algorithms used for Machine
Learning and its applications. We begin with a broader definition of machine learning and …

Learning to classify time series with limited data is a practical yet challenging problem.
Current methods are primarily based on hand-designed feature extraction rules or domain …

The beginning of the age of artificial intelligence and machine learning has created new
challenges and opportunities for data analysts, statisticians, mathematicians …

Failures of railway point systems (RPSs) often lead to service delays or hazardous
situations. A condition monitoring system can be used by railway infrastructure operators to …

In recent years there has been growing interests in mining time series data. To overcome the
adverse influence of time shift, a number of effective elastic matching approaches such as …

Multivariate time series classification is a critical problem in data mining with broad
applications. It requires harnessing the inter-relationship of multiple variables and various …

In this paper, we propose support vector-based supervised learning algorithms, called
multiclass support vector data description with weighted dynamic time war** kernel …

This paper proposes some extensions to the work on kernels dedicated to string or time
series global alignment based on the aggregation of scores obtained by local alignments …

Welding defect detection in the manufacturing of hot water tanks is still often performed by
human visual inspection or with the help of classical non-destructive tests such as liquid …