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Over 85%% phoneme recognition accuracy is demonstrated for speaker-dependent learning from fluent, large-vocabulary speech. 1 Automatic Discovery of Phone me)s Statistical models learnt from data are extensively used in modern automatic speech recognition (ASR) systems.

Predictive performance of the models derived from the sparse data sets were compared with models learnt from the complete data sets to assess how much performance was lost through data removal.

Table 5 Action recognition accuracy of our individually normalizing method for IXMAS dataset using the models learnt from HumanEva dataset compared with the method prosed in [[43]] Accuracy Punch Wave Throw a ball Walk All actions Ind-normal 75.0 75.0 75.075.075.0 [43] 86.8 75.0 82.4 75.0 82.2.

We define the Block instability noted BIS for a pair of hyperparameters by measuring how the two Jacobian matrices built from two models learnt from two different subsamples differ in average.

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Adaptive models learn from battery-performance data and thereby self-adjust to reduce errors.

Scheiter, S., Langan, L. & Higgins, S. I. Next-generation dynamic global vegetation models: learning from community ecology.

These models learned from solutions calculated by a validated multibody model of the knee.

We formulate and develop a control method for homogeneous charge compression ignition (HCCI) engines using model predictive control (MPC) and models learned from operational data.

Statistical shape models learned from a population of previously observed training shapes are nowadays widely used in medical image analysis to aid segmentation or classification.

Einstein's customizable models learn from every interaction to deliver valuable insights and predictions to companies of all sizes.

Table 13 Performance of models learned from the CHEMDNER training set when evaluated on the development set.

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