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A Standardized Probability Comparison Approach for Evaluating and Combining Pixel-based Classification Procedures Dongle Chen Abstract In this paper, a standardized probability approach is presented

Dongle Chen Implementation of a Classifier for Image Feature Clustering with Unsupervised Learning by RIE J. Huang, Peter M. B. Piano Abstract The classification tasks can be very diverse, such as image classification and feature extraction from images. In this paper, we presented a classifier for classifying image features within the feature space, which are known to be relevant in image classification. We proposed to use unsupervised learning method which uses the classification task as a dataset or training set. Unsupervised learning algorithms such as neural networks, recurrent nets, etc., can be easily implemented directly inside a neural network, and can solve image classification task. We investigated the performance of the classification model on 10,000 images on the COCO dataset. Our results showed that our classifier was the most effective classifier among all the others and exhibited maximum classifier performance when using COCO dataset. This work provides a practical and efficient solution that is both scalable and efficient. Xiaoping HU, Xiaomi Zen, While Zen, Songhai Li Abstract In this work, we propose to use a machine learning framework to study image classification. We propose an unsupervised learning method for image classification, to understand the task of image classification and to discover useful features. We proposed to use unsupervised, non-linear model that makes learning in high dimensional environment easily. Our paper introduces a novel data-driven approach that allows to analyze the neural network model for face detection and provides a high-quality result of our unsupervised model. Our findings of this paper show that this new unsupervised model makes learning of deep neural networks easier. We also investigated the face detection result and show the result of our data-driven analysis. Xiaoping Huang, Xiaomi Zen, While Zen Abstract In face detection problem there are many important factors that need accurate and stable prediction to perform optimal face detection. We present two new deep learning architectures and explain the learning process of each of them. We then extend neural net architecture of a 3D face detection system named Tensor board to a new deep learning architecture called Facet that can perform better than both Connect and Pooling CNN on large images. Furthermore, we also present a new unsupervised solution from this work; its classifiers perform very well on large datasets.

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