Cortical-based hierarchical clustering algorithm for image classification


Cortical-based hierarchical clustering algorithm for image classification – A key problem in many computer vision applications is the detection or segmentation of unknown objects from image. In order to tackle this challenge, we propose a novel and efficient clustering algorithm for object identification and extraction. The key idea is to first learn a graph-theoretic model of different clusters, and then train a Convolutional Neural Net (CNN) to recognize unknown objects (cannot distinguish any object). In this work, we propose a novel supervised learning approach that trains CNN-based models, and then optimizes their performance by minimizing the dimension of the network and the number of objects. We demonstrate that this approach outperforms both traditional clustering methods and our best-known segmentation method, i.e. kLSTM-SVM, in several real-world datasets, and is faster than the best known method.

In this work, we propose a novel general framework for the integration of natural language knowledge with the state of the art learning algorithms in an integrated manner. We provide a new technique for using state of the art learned representations to provide a principled approach to represent the state of the art performance of natural language processing systems. We demonstrate the effectiveness of the new technique on several publicly available datasets including MNIST and COCO, and show that our new technique significantly outperforms the existing techniques on both datasets. The proposed framework can be regarded as a tool for the integration of knowledge about how humans perform in complex situations, which is particularly relevant for the task of natural language processing with complex models. To this end, we extend the framework to model natural language learning with the state of the art neural network architecture.

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Cortical-based hierarchical clustering algorithm for image classification

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  • Randomized Convexification of Learning Rates and Logarithmic Rates

    Learning Scene Similarity by Embedding Concepts in Deep Neural NetworksIn this work, we propose a novel general framework for the integration of natural language knowledge with the state of the art learning algorithms in an integrated manner. We provide a new technique for using state of the art learned representations to provide a principled approach to represent the state of the art performance of natural language processing systems. We demonstrate the effectiveness of the new technique on several publicly available datasets including MNIST and COCO, and show that our new technique significantly outperforms the existing techniques on both datasets. The proposed framework can be regarded as a tool for the integration of knowledge about how humans perform in complex situations, which is particularly relevant for the task of natural language processing with complex models. To this end, we extend the framework to model natural language learning with the state of the art neural network architecture.


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