We describe a novel extension of the Poisson regression model to be based on a multi-layer perceptron, a type of neural network. This relaxes the assumptions of the traditional Poisson regression model, while including it as a special case. In this paper, we describe neural network regression models with six different schemes and compare their performances in three simulated data sets, namely one linear and two nonlinear cases. From the simulation study it is found that the Poisson regression models work well when the linearity assumption is correct, but the neural network models can largely improve the prediction in nonlinear situations.
Previous studies have shown controversial results about the role of androgens in coronary artery disease (CAD). We performed this study to examine and compare the relationship between androgenic hormones and CAD using conventional linear statistical techniques as well as novel non-linear approaches. The study was conducted on 502 consecutive men who were referred for selective coronary angiography at Tehran Heart Center due to different indications. We studied the relationship between androgenic hormones and CAD by using the generalized linear models, generalized additive models, and neural networks. Free testosterone (fT), total testosterone (tT) and dehydroepiandrosterone sulfate levels in patients with significant CAD versus normal individuals were 6.69 +/- 3.20 pg/ml, 16.60 +/- 6.66 nm/l, and 113.38 +/- 72.9 microg/dl versus 7.12 +/- 3.58 pg/ml, 15.82 +/- 7.26 nm/l, and 109.03 +/- 68.19 microg/dl, respectively (P > 0.05). The Generalized linear models was unable to show any significant relationship between androgenic hormones and CAD, while generalized additive model and neural networks supported the significant effect of androgenic hormones on CAD. This finding suggests a nonlinear association of tT levels with CAD: lower levels have a preventive effect on CAD, whereas higher values increase the risk of CAD. Emphasizing the non-linearity of the variables may provide new insight into the possible explanation of the effect of androgenic hormones on CAD.
The issue of input variability resulting from speaker changes is one of the most crucial factors influencing the effectiveness of speech recognition systems. A solution to this problem is adaptation or normalization of the input, in a way that all the parameters of the input representation are adapted to that of a single speaker, and a kind of normalization is applied to the input pattern against the speaker changes, before recognition. This paper proposes three such methods in which some effects of the speaker changes influencing speech recognition process is compensated. In all three methods, a feed-forward neural network is first trained for mapping the input into codes representing the phonetic classes and speakers. Then, among the 71 speakers used in training, the one who is showing the highest percentage of phone recognition accuracy is selected as the reference speaker so that the representation parameters of the other speakers are converted to the corresponding speech uttered by him. In the first method, the error back-propagation algorithm is used for finding the optimal point of every decision region relating to each phone of each speaker in the input space for all the phones and all the speakers. The distances between these points and the corresponding points related to the reference speaker are employed for offsetting the speaker change effects and the adaptation of the input signal to the reference speaker. In the second method, using the error back-propagation algorithm and maintaining the reference speaker data as the desirable speaker output, we correct all the speech signal frames, i.e., the train and the test datasets, so that they coincide with the corresponding speech of the reference speaker. In the third method, another feedforward neural network is applied inversely for mapping the phonetic classes and speaker information to the input representation. The phonetic output retrieved from the direct network along with the reference speaker data are given to the inverse network. Using this information, the inverse network yields an estimation of the input representation adapted to the reference speaker. In all three methods, the final speech recognition model is trained using the adapted training data, and is tested by the adapted testing data. Implementing these methods and combining the final network results with un-adapted network based on the highest confidence level, an increase of 2.1, 2.6 and 3% in phone recognition accuracy on the clean speech is obtained from the three methods, respectively.
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