Recurrent and Recurrent Regression Models for Nonconvex and Non-convex Penalization – We propose a neural model for a general purpose binary classification problem. The neural model is a deep neural network that learns to predict the binary classes, with several training samples collected during training. The model is trained with a set of samples collected from one or multiple classification problems, and learns to predict the binary classes in an ensemble of a novel set of experiments. Experimental results demonstrate that our model achieves state of the art performance in terms of classification accuracy, with a good accuracy in both binary classification accuracy and classification accuracy while the proposed model is in continuous exploration mode. Since the proposed model is not trained on any specific binary class, it is not restricted to a specific class, this makes it a better candidate for practical use. The experimental results also demonstrate that the proposed model can be extended to handle multiple classes.

We present an adaptive sparse coding of neural networks to classify complex objects. With adaptive sparse coding, neurons in the input layer are connected to the global network of synaptic weights. In this way, if the network can be modelled on a given model, an adaptive coding system can be developed, based on such a network. We show that this adaptive coding scheme is more efficient than the model-based one by approximately solving the problem of learning sparse coding in a non-linear fashion. In particular, for an adaptive sparse coding system, an adaptive coding neural network can be trained using recurrent neural networks, without using any prior information on the current model.

Multi-class Classification Using Kernel Methods with the Difference Longest Common Vectors

Fast k-Nearest Neighbor with Bayesian Information Learning

# Recurrent and Recurrent Regression Models for Nonconvex and Non-convex Penalization

Unsupervised learning of object features and hierarchy for action recognition

The Multi-Domain VisionNet: A Large-scale 3D Wide-RoboDetector Dataset for Pathological Lung Nodule DetectionWe present an adaptive sparse coding of neural networks to classify complex objects. With adaptive sparse coding, neurons in the input layer are connected to the global network of synaptic weights. In this way, if the network can be modelled on a given model, an adaptive coding system can be developed, based on such a network. We show that this adaptive coding scheme is more efficient than the model-based one by approximately solving the problem of learning sparse coding in a non-linear fashion. In particular, for an adaptive sparse coding system, an adaptive coding neural network can be trained using recurrent neural networks, without using any prior information on the current model.