textabstractWhen a Convolutional Neural Network is used for on-the-fly evaluation of continuously updating time-sequences, many redundant convolution operations are performed. We propose the method ...

With the biomedical field generating large quantities of time series data, there has been a growing interest in developing and refining machine learning methods that allow its mining and exploitation....

While the research in affective computing has been exclusively dealing with the recognition of explicit affective and cognitive states, carefully designed psychological and neuroimaging studies indica...

We suggest a simple yet effective and parameter-free feature construction process for time series classification. Our process is decomposed in three steps: (i) we transform original data into several ...

We propose a new approach to gesture recognition using the properties of Spherical Self-Organizing Map (SSOM). Unbounded mapping of data onto a SSOM creates not only a powerful tool for visualization ...

We plan to recommend some initial suitable single-itemed sequences like a flight itinerary based on a preference pattern in the form of personalized sequential pattern to each cold-start user. However...

We introduce an adaptive framework for multivariate sensor stream data reduction. The proposed method takes as input a sliding window of multivariate stream data, classifies the data in each window, a...

We introduce a classification framework for continuous multivariate stream data. The proposed approach works in two steps. In the preprocessing step, it takes as input a sliding window of multivariate...

We improve DTW distance measure in multivariate time series classification.We use derivatives to improve DTW in multivariate time series classification.We test effectiveness on 18 real time series.We ...

Unconscious mental processes have recently started gaining attention in a number of scientific disciplines. One of the theoretical frameworks for describing unconscious processes was introduced by Jun...

Transient classification is the problem of identifying and classifying temporary phenomena in astronomical data. These phenomena are caused by extreme physical processes such as supernova explosions u...

Time series modeling is an important problem with many applications in different domains. Here we consider discriminative learning from time series, where we seek to predict an output response variabl...

Time series is an important class of temporal data objects and it can be easily obtained from scientific and financial applications. A time series is a collection of observations made chronologically....

Time series classification is a supervised learning problem aimed at labeling temporally structured multivariate sequences of variable length. The most common approach reduces time series classificati...

Time series classification is a field which has drawn much attention over the past decade. A new approach for classification of time series uses classification trees based on shapelets. A shapelet is ...

This work studies the impact of using dynamic information as features in a machine learning algorithm for the prediction task of classifying critically ill patients in two classes according to the tim...

This work studies the impact of using dynamic information as features in a machine learning algorithm for the prediction task of classifying critically ill patients in two classes according to the tim...

This work presents a novel approach to multivariate time series classification. The method exploits the multivariate structure of the time series and the possibilities of the stacking ensemble method....

This work focuses on early classification of ongoing observation of the object, which is beneficial for a number of applications that require time-critical decision making. We propose an approach for ...

This work applies a variety of multilinear function factorisation techniques to extract appropriate features or attributes from high dimensional multivariate time series for classification. Recently, ...