Contributions to decision tree induction: bias/variance tradeoff and time series classification

Synthesis of the performance of LB in the case of the sphere.Convergence rate of EMNA with QR mutations (as in [14, 12]), which pro- vide the state of the art convergence rates for

To allow time series comparison while dealing with time delays, numerous alignments strategies have been proposed, as the ones based on Dynamic Time Warping (DTW) [1], which

Then, the defined oracle, whose relevance was empirically confirmed on realistic channels in a millimeter wave massive MIMO context, was compared to the classical LMMSE

EVO-Tree (EVOlutionary Algorithm for Decision Tree Induction) is a novel multi- objective evolutionary algorithm proposed to evolve binary decision trees for

It demonstrates two things: firstly, tuning the shapelets then classifying with the LS model (Hybrid-LR) makes no significant difference when compared to ST alone; and secondly,

Section (3) has outlined the general framework for the early classification prob- lem while Section (4) has presented our proposed approach where the problem is cast as a

We find that a variation of the DTW kernel: the global alignment (GA) kernel wins when the time series are short in length (<100 temporal dimensions) but when the time series get

Second resubmission step: “PELICAN: deeP architecturE for the LIght Curve ANalysis” Johanna Pasquet, J´ erˆ ome Pasquet, Marc Chaumont and Dominique Fouchez. Astronomy