Relevance Estimation Using Sparse Multidimensional Scaling: Application to Classification and Regression – We present a method to use unsupervised feature learning (similar to Sparse Multi-Class Classification) over large class images (e.g., MNIST and CIFAR-10). Under certain assumptions about the image representations, we establish a new classifier for the task of classification over the MNIST dataset. In this paper, we describe our method, show how it can be used to detect and model the unseen classes and its class labels, and obtain efficient classification results. We demonstrate our method on the MNIST classification task, which achieved state-of-the-art performance when compared to the model-based classification approach, and we show the effectiveness of the proposed method by using different methods to separate the unseen classes in each dataset, and to model the unseen class labels.

In this manuscript we extend the existing classification algorithm with deep networks. We make use of recent deep architectures for supervised learning. The basic idea is to learn features from the data. Deep-learning networks are well suited to this task. Experiments with various datasets demonstrate that deep networks offer improved classification performance compared to state-of-the-art deep architectures.

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Relevance Estimation Using Sparse Multidimensional Scaling: Application to Classification and Regression

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Boosting for Deep Supervised Learning

CNN-based Classification for Improved Automatic Pancreas ExtractionIn this manuscript we extend the existing classification algorithm with deep networks. We make use of recent deep architectures for supervised learning. The basic idea is to learn features from the data. Deep-learning networks are well suited to this task. Experiments with various datasets demonstrate that deep networks offer improved classification performance compared to state-of-the-art deep architectures.