Interval and dynamic time warping-based decision trees
Proceedings of the 2004 ACM symposium on Applied computing
A PCA-based similarity measure for multivariate time series
Proceedings of the 2nd ACM international workshop on Multimedia databases
Vision based GUI for interactive mobile robots
Proceedings of the 10th international conference on Intelligent user interfaces
Feature Subset Selection and Feature Ranking for Multivariate Time Series
IEEE Transactions on Knowledge and Data Engineering
On the Stationarity of Multivariate Time Series for Correlation-Based Data Analysis
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
ICML '06 Proceedings of the 23rd international conference on Machine learning
An efficient k nearest neighbor search for multivariate time series
Information and Computation
Image and video for hearing impaired people
Journal on Image and Video Processing
Recognising Human Emotions from Body Movement and Gesture Dynamics
ACII '07 Proceedings of the 2nd international conference on Affective Computing and Intelligent Interaction
Distribution-based similarity measures for multi-dimensional point set retrieval applications
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Constraint-Based Learning of Distance Functions for Object Trajectories
SSDBM 2009 Proceedings of the 21st International Conference on Scientific and Statistical Database Management
Component-based discriminative classification for hidden Markov models
Pattern Recognition
Categorizing classes of signals by means of fuzzy gradual rules
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Support vector machines of interval-based features for time series classification
Knowledge-Based Systems
Automatic recognition of hand gestures with differential evolution
Evo'08 Proceedings of the 2008 conference on Applications of evolutionary computing
Which spatial partition trees are adaptive to intrinsic dimension?
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Margin-closed frequent sequential pattern mining
Proceedings of the ACM SIGKDD Workshop on Useful Patterns
A brief survey on sequence classification
ACM SIGKDD Explorations Newsletter
Hand gesture recognition based on segmented singular value decomposition
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part II
Optimizing hierarchical temporal memory for multivariable time series
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part II
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part I
Correlation based dynamic time warping of multivariate time series
Expert Systems with Applications: An International Journal
Decision forest: an algorithm for classifying multivariate time series
International Journal of Business Intelligence and Data Mining
Order-Preserving sparse coding for sequence classification
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Max-Margin Early Event Detectors
International Journal of Computer Vision
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Machine learning research has, to a great extent, ignored an important aspect of many real world applications: time. Existing concept learners predominantly operate on a static set of attributes; for example, classifying flowers described by leaf size, petal colour and petal count. However, many real datasets are not static; they cannot sensibly be represented as a fixed set of attributes. Rather, the examples are expressed as features that vary temporally, and it is the temporal variation itself that is used for classification. Consider a simple gesture recognition domain, in which the temporal features are the position of the hands, finger bends, and so on. Looking at the position of the hand at one point in time is not likely to lead to a successful classification; it is only by analysing changes in position that recognition is possible. This thesis presents a new technique for temporal classification. By extracting sub-events from the training instances and parameterising them to allow feature construction for a subsequent learning process, it is able to employ background knowledge and express learnt concepts in terms of the background knowledge. The novel results of the thesis are: a temporal learner capable of producing comprehensible and accurate classifiers for multivariate time series that can learn from a small number of instances and can integrate non-temporal features; a feature construction technique that parameterises sub-events of the training set and clusters them to construct features for a propositional learner; and a technique for post-processing classification rules produced by the learner to give a comprehensible description expressed in the same form as the original background knowledge. The thesis discusses the implementation of TClass, a temporal learner. Results show rules that are comprehensible in many cases and accuracy results close to or better than existing technique—over 98 per cent for sign language and 72 per cent for ECGs (equivalent to the accuracy of a human cardiologist). One further surprising result is that a small set of very primitive sub-events proves to be functional, avoiding the need for labour-intensive background knowledge if it is not available.