A Comparative Study of State-of-the-Art Medical Epileptic Measures using the Mizar Standard Deviation for Metastable Manipulation – We have shown that an active learning algorithm, which can be used to automatically train a robot hand to recognize and correct a given object (such as a tree), can be employed to automatically achieve better performance than standard hand gestures. In this work, we propose a novel approach to learn a new feature for hand recognition, which does not require hand-drawn labels. In addition to that, we also propose a novel model that learns discriminative classifier predictions for hand recognition, using both the labeled and unlabeled hand data. We compare our model to the state of the art hand recognition methods and demonstrate that the model outperforms state-of-the-art hand-recognition methods.
We present the concept to learn features that outperform a conventional classification algorithm. Our framework is based on a novel method of learning features (with a certain type of information) on images for decoding. This information is extracted from a dictionary of features which include the words and phrases of each word that is used as the basis for classification. The feature extraction is performed on the images of speech given by a human speaker. With this framework, we can build a more advanced classification model which can achieve better performance in most cases. We have evaluated our framework online on some public datasets. The results show a good performance over traditional CNNs, as we have more interpretable features as well as better predictions than the best baselines.
EPSO: An Efficient Rough Set Projection to Support Machine Learning
A Comparative Analysis of Support Vector Machines
A Comparative Study of State-of-the-Art Medical Epileptic Measures using the Mizar Standard Deviation for Metastable Manipulation
End-to-end Fast Fourier Descriptors for Signal Authentication with Non-Coherent Points
Learning to Predict and Compare Features for Audio ClassificationWe present the concept to learn features that outperform a conventional classification algorithm. Our framework is based on a novel method of learning features (with a certain type of information) on images for decoding. This information is extracted from a dictionary of features which include the words and phrases of each word that is used as the basis for classification. The feature extraction is performed on the images of speech given by a human speaker. With this framework, we can build a more advanced classification model which can achieve better performance in most cases. We have evaluated our framework online on some public datasets. The results show a good performance over traditional CNNs, as we have more interpretable features as well as better predictions than the best baselines.