Results 261 to 270 of about 576,901 (299)
Time Series Classification of Electroencephalography Data [PDF]
, 2023 Electroencephalography (EEG) is a non-invasive technique used to record the electrical activity of the brain using electrodes placed on the scalp. EEG data is commonly used for classification problems. However, many of the current classification techniques are dataset specific and cannot be applied to EEG data problems as a whole. We propose the use of
Aiden Rushbrooke +3 more
openaire +2 more sources
Some of the next articles are maybe not open access.
Related searches:
Related searches:
Efficient Classification of Long Time-Series
2013Time-series classification has gained wide attention within the Machine Learning community, due to its large range of applicability varying from medical diagnosis, financial markets, up to shape and trajectory classification. The current state-of-art methods applied in time-series classification rely on detecting similar instances through neighboring ...
Josif Grabocka +2 more
openaire +1 more source
Semi-supervised time series classification
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, 2006The problem of time series classification has attracted great interest in the last decade. However current research assumes the existence of large amounts of labeled training data. In reality, such data may be very difficult or expensive to obtain. For example, it may require the time and expertise of cardiologists, space launch technicians, or other ...
Li Wei 0001, Eamonn J. Keogh
openaire +1 more source
Description and classification of granular time series
Soft Computing, 2014The study is concerned with a concept and a design of granular time series and granular classifiers. In contrast to the plethora of various models of time series, which are predominantly numeric, we propose to effectively exploit the idea of information granules in the description and classification of time series.
Rami Al-Hmouz +3 more
openaire +1 more source
Fuzzy classification of time series data
2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2013The problem of classification of time series data is an interesting problem in the field of data mining. Even though several algorithms have been proposed for the problem of time series classification we have developed an innovative algorithm which is computationally fast and accurate in several cases when compared with 1NN classifier. In our method we
Penugonda Ravikumar, V. Susheela Devi
openaire +1 more source
Sequential classification of MODIS time series
2012 IEEE International Geoscience and Remote Sensing Symposium, 2012In this paper a sequential time-varying maximum likelihood classifier is applied on coarse resolution MODIS surface reflectance data. It is shown that good class separability can be obtained after considering only one year of data. Finally it is shown that after NDVI, band 2 has the highest separability of all the MODIS land bands, and band 6 has the ...
Trienko L. Grobler +5 more
openaire +1 more source
Time Series based Gastropod Classification
2018 10th International Conference on Knowledge and Smart Technology (KST), 2018This paper presents Series-K, an automatic Gastropod classification system based on Time Series and k-Nearest Neighbor. Species evolve over time. Biologists have discovered, attempted to describe and put them into categories. Our proposed method automatically processes gastropods’ images to help the malacologists accurately identify gastropods into ...
Janya Onpans +2 more
openaire +1 more source
Reliable early classification of time series
2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012Early classification of time series is important in time-sensitive applications. An approach is presented for early classification using generative classifiers with the dual objectives of providing a class label as early as possible while guaranteeing with high probability that the early class matches the class that would be assigned to a longer time ...
Hyrum S. Anderson +3 more
openaire +1 more source
Embedding Time Series Data for Classification
2005We propose an approach to embed time series data in a vector space based on the distances obtained from Dynamic Time Warping (DTW), and to classify them in the embedded space. Under the problem setting in which both labeled data and unlabeled data are given beforehand, we consider three embeddings, embedding in a Euclidean space by MDS, embedding in a ...
Akira Hayashi +2 more
openaire +1 more source
Stacking for multivariate time series classification
Pattern Analysis and Applications, 2013This 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. The basics of the method may be described in three steps: first, decomposing the multivariate time series on its constituent univariate time series ...
Oscar J. Prieto +2 more
openaire +1 more source