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Time series classification is an important task in data mining that has been traditionally addressed with the use of similarity-based classifiers. The 1-NN DTW is typically considered the most accurate model for temporal data. Nevertheless, some authors have recently proposed ingenious alternatives to the 1-NN DTW by using diversity of time series representation or by using DTW for feature extraction...
Models were developed to classify six different behaviours for a group of seven steers fitted with an accelerometer and pressure sensor. As part of the process, a greedy feature selection method was used to identify the most discriminatory inputs from a diverse set of statistical, spectral and information theory based features. The study showed the second order statistic features (standard deviation...
One of the effective methodologies for time series classification is to identify informative subsequence patterns in time series and exploit them as discriminative features. Previous studies on this methodology have achieved promising results using a small number of individually selected patterns. However, there remain difficulties in finding a set of related patterns or patterns of a minor class,...
A new multiple kernel learning (MKL) framework is presented for classification of satellite remotely sensed time series for agricultural analysis. In this MKL framework, a new composite kernel is constructed with a weighted sum of some predefined kernels. The problem of proper estimation of weights is modeled as an optimization problem of maximizing the kernel alignment between composite kernel and...
There are two paradigms for modeling varying length time series data, namely, modeling the sequence of feature vectors and modeling the sets of vectors. In this paper, we propose a regression based autoassociative model for modeling sets of vectors for time series data. We also propose a hybrid framework where a regression based autoassociative model is used for representing varying length time series...
The increased availability of time series datasets prompts the development of new tools and methods that allow machine learning classifiers to better cope with time series data. Time series data are usually characterized by a high space dimensionality and a very strong correlation among features. This special nature makes the development of effective time series classifiers a challenging task. This...
Visual categorization problems, such as object classification or action recognition, are increasingly often approached using a detection strategy: a classifier function is first applied to candidate subwindows of the image or the video, and then the maximum classifier score is used for class decision. Traditionally, the subwindow classifiers are trained on a large collection of examples manually annotated...
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