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Found 10 papers, 4 papers with code
Open challenges for Machine Learning based Early Decision-Making research
However, the later a decision is made, the more its accuracy tends to improve, since the description of the problem to hand is enriched over time.
On the separation of shape and temporal patterns in time series -Application to signature authentication-
We propose to exploit and adapt a probabilistic temporal alignment algorithm, initially designed to estimate the centroid of a set of time series, to build some heuristicelements of solution to this separation problem.
Sparsification of the Alignment Path Search Space in Dynamic Time Warping
This work addresses the sparsification of the alignment path search space for DTW-like measures, essentially to lower their computational cost without loosing on the quality of the measure.
Hybrid Isolation Forest - Application to Intrusion Detection
From the identification of a drawback in the Isolation Forest (IF) algorithm that limits its use in the scope of anomaly detection, we propose two extensions that allow to firstly overcome the previously mention limitation and secondly to provide it with some supervised learning capability.
Times series averaging and denoising from a probabilistic perspective on time-elastic kernels
In the light of regularized dynamic time warping kernels, this paper re-considers the concept of time elastic centroid for a setof time series.
Adaptive Down-Sampling and Dimension Reduction in Time Elastic Kernel Machines for Efficient Recognition of Isolated Gestures
In return, very few of these methods have explicitly addressed the dimensionality reduction along the time axis.
Times series averaging from a probabilistic interpretation of time-elastic kernel
An experimentation that compares for 45 time series datasets classification error rates obtained by first near neighbors classifiers exploiting a single medoid or centroid estimate to represent each categories show that: i) centroids based approaches significantly outperform medoids based approaches, ii) on the considered experience, the two proposed algorithms outperform the state of the art DBA algorithm, and iii) the second proposed algorithm that implements an averaging jointly in the sample space and along the time axes emerges as the most significantly robust time elastic averaging heuristic with an interesting noise reduction capability.
Exploiting a comparability mapping to improve bi-lingual data categorization: a three-mode data analysis perspective
Our experiments show clear improvements in clustering and classification accuracies when mixing comparability with similarity measures, with, as expected, a higher robustness obtained when the two comparability variant measures that we propose are used.
Down-Sampling coupled to Elastic Kernel Machines for Efficient Recognition of Isolated Gestures
In the field of gestural action recognition, many studies have focused on dimensionality reduction along the spatial axis, to reduce both the variability of gestural sequences expressed in the reduced space, and the computational complexity of their processing.
On Recursive Edit Distance Kernels with Application to Time Series Classification
The classification experiment we conducted on three classical time warp distances (two of which being metrics), using Support Vector Machine classifier, leads to conclude that, when the pairwise distance matrix obtained from the training data is \textit{far} from definiteness, the positive definite recursive elastic kernels outperform in general the distance substituting kernels for the classical elastic distances we have tested.
