A Bayesian Network Based Discrepancy Mechanism – This article analyses the model for the prediction of the global minimum and the global maximum in an online setting that has the following features: (i) the prediction of the global minimum is known before and (ii) the global maximum in the online setting is known after. The model is able to infer the true global minimum from the estimated global maximum (based on the assumption on the expected data distribution of the global minimum).
This paper presents a new technique for encoding a set of binary data by using a new class of features derived from a Gaussian process. The first two measures used to define the parameters of the Gaussian process are called Gaussian process features and are used to evaluate the classification performance of the data. The third measure, referred to as maximum likelihood, is used to quantify the accuracy of the predictions of the Gaussian process. The experimental results show that the proposed method can achieve better classification performance than previous works in the literature. Moreover, the proposed method is able to learn features for binary classification even if the number of binary classes do not exceed the expected class level.
We present a technique for visual-emotional matching of emotional and physiological data by means of the deep neural network. The network is trained without visual signals, and the resulting visual correspondences are encoded in multiple layers. We also present the method of constructing representations for data, and use them to encode emotional expressions and physiological signals. When the training data is stored in a database, the network predicts the emotional expression, and uses the extracted representations to improve the match quality.
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A Bayesian Network Based Discrepancy Mechanism
Super-Convergence: Surpassing Convergence Rate of the Sparse Coding MethodThis paper presents a new technique for encoding a set of binary data by using a new class of features derived from a Gaussian process. The first two measures used to define the parameters of the Gaussian process are called Gaussian process features and are used to evaluate the classification performance of the data. The third measure, referred to as maximum likelihood, is used to quantify the accuracy of the predictions of the Gaussian process. The experimental results show that the proposed method can achieve better classification performance than previous works in the literature. Moreover, the proposed method is able to learn features for binary classification even if the number of binary classes do not exceed the expected class level.
We present a technique for visual-emotional matching of emotional and physiological data by means of the deep neural network. The network is trained without visual signals, and the resulting visual correspondences are encoded in multiple layers. We also present the method of constructing representations for data, and use them to encode emotional expressions and physiological signals. When the training data is stored in a database, the network predicts the emotional expression, and uses the extracted representations to improve the match quality.
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