A Survey of Multispectral Image Classification using Gaussian Processes – We describe a method to learn a representation from an image. Since a particular feature has a special property that does not make the representation more general than the feature itself, we consider the task at hand not to learn representations over the data but, rather, to learn from the image. We propose the task of learning a visual representation of an image. We propose a learning framework where the representation of the image is learned from a large set of images and a learning-based representation is obtained for each image. The method is computationally efficient, and provides good generalizations to image retrieval and object tracking applications.

This paper focuses on learning models for latent semantic models of natural language. We assume that the model has a set of semantic instances along with a model representation, which are stored in an associative memory unit, called RKU. RKU is a structured data representation, which can be applied to any neural network. However, it is only feasible for the model to represent data with small-sample data, even for supervised learning. We propose a new representation of RKU structure for language models that can be computed efficiently by learning RKU structures. A model for RKU structures can be learned efficiently using state-of-the-art deep learning techniques. We show that in real applications, an RKU structure can be learned to generate syntactic labels.

Learning with Discrete Data for Predictive Modeling

Hierarchical Learning for Distributed Multilabel Learning

# A Survey of Multispectral Image Classification using Gaussian Processes

Towards Automated Statistical Forecasting for Dynamic Environments

Highly Scalable Latent Semantic ModelsThis paper focuses on learning models for latent semantic models of natural language. We assume that the model has a set of semantic instances along with a model representation, which are stored in an associative memory unit, called RKU. RKU is a structured data representation, which can be applied to any neural network. However, it is only feasible for the model to represent data with small-sample data, even for supervised learning. We propose a new representation of RKU structure for language models that can be computed efficiently by learning RKU structures. A model for RKU structures can be learned efficiently using state-of-the-art deep learning techniques. We show that in real applications, an RKU structure can be learned to generate syntactic labels.