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What is … = 5732  
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0.632 Boostrap  Sampling with replacement. Each data point has probability (1 – 1/n)n of being selected as test data: Training data = 1 – (1 – 1/n)n of the original data. A particular training data has a probability of (1 – 1/n) of not being picked. This means the training data will contain approximately 63.2% of the instances. 
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1NearestNeighborBased Multiclass Learning  This paper deals with NearestNeighbor (NN) learning algorithms in metric spaces. Initiated by Fix and Hodges in 1951 , this seemingly simplistic learning paradigm remains competitive against more sophisticated methods and, in its celebrated kNN version, has been placed on a solid theoretical foundation. Although the classic 1NN is well known to be inconsistent in general, in recent years a series of papers has presented variations on the theme of a regularized 1NN classifier, as an alternative to the Bayesconsistent kNN. Gottlieb et al. showed that approximate nearest neighbor search can act as a regularizer, actually improving generalization performance rather than just injecting noise. In a followup work, showed that applying Structural Risk Minimization to (essentially) the marginregularized datadependent bound in yields a strongly Bayesconsistent 1NN classifier. A further development has seen marginbased regularization analyzed through the lens of sample compression: a nearoptimal nearest neighbor condensing algorithm was presented and later extended to cover semimetric spaces ; an activized version also appeared. As detailed in , marginregularized 1NN methods enjoy a number of statistical and computational advantages over the traditional kNN classifier. Salient among these are explicit datadependent generalization bounds, and considerable runtime and memory savings. Sample compression affords additional advantages, in the form of tighter generalization bounds and increased efficiency in time and space. 
1ofn Code  A special case of constant weight codes are the oneofN codes, that encode log_2 N bits in a codeword of N bits. The oneoftwo code uses the code words 01 and 10 to encode the bits ‘0’ and ‘1’. A oneoffour code can use the words 0001, 0010, 0100, 1000 in order to encode two bits 00, 01, 10, and 11. ACDm 
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2PDNN  Machine Learning as a Service (MLaaS), such as Microsoft Azure, Amazon AWS, offers an effective DNN model to complete the machine learning task for small businesses and individuals who are restricted to the lacking data and computing power. However, here comes an issue that user privacy is exposed to the MLaaS server, since users need to upload their sensitive data to the MLaaS server. In order to preserve their privacy, users can encrypt their data before uploading it. This makes it difficult to run the DNN model because it is not designed for running in ciphertext domain. In this paper, using the Paillier homomorphic cryptosystem we present a new PrivacyPreserving Deep Neural Network model that we called 2PDNN. This model can fulfill the machine leaning task in ciphertext domain. By using 2PDNN, MLaaS is able to provide a PrivacyPreserving machine learning service for users. We build our 2PDNN model based on LeNet5, and test it with the encrypted MNIST dataset. The classification accuracy is more than 97%, which is close to the accuracy of LeNet5 running with the MNIST dataset and higher than that of other existing PrivacyPreserving machine learning models 
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