As location verification process is based on machine learning, all estimates come with errors, thus the probability that CSP is in the inferred zone grows with the zone size.
For the actual classification of new tweets we then let the machine learning classifiers estimate the probabilities P(a n g e r| f1,…, f n ), P(f e a r| f1,…, f n ), and P(p o s i t i v e| f1,…, f n ), where f1,…, f n refers to the used feature vector extracted from the tweet we want to classify.
We applied the local minimax prediction algorithms based on the Improved Tikhonov Estimator for kernel machine learning to estimate a given patient's probability of having cancer.
Finally, Support Vector regression (SVR) as machine learning algorithm that estimates the thickness of the soft tissue layer is briefly introduced.
Therefore, Prenaut uses simulation data and applies machine learning methods for estimating design parameters.
For the second case, we use machine learning techniques to estimate the probability of objects supporting each other.
We also employed a suite of supervised machine learning algorithms to estimate the predictive capacity of our models.
In [12], the authors proposed a machine learning approach to estimate the optimal solution of linking neuron fragments.
A machine learning approach for estimating the depth map for 2D video sequences is proposed in [3].
In this paper we propose a machine learning technique to estimate heart-rate from electrocardiogram (ECG) data collected using wearable devices.
