We present a new encoder-decoder Vision Transformer architecture, Patcher, for medical image segmentation. Unlike standard Transformers, it employs Patcher blocks that segment an into large patches, each of which is further divided small patches. Transformers are applied to the patches within patch, constrains receptive field pixel. intentionally make overlap enhance intra-patch communication. ...

In this study, the automated diagnostic systems employing diverse and composite features for electrocardiogram (ECG) signals were analyzed and their accuracies were determined. In pattern recognition applications, diverse features are extracted from raw data which needs recognizing. Combining multiple classifiers with diverse features are viewed as a general problem in various application areas...

In this paper, we show how a topographic mapping can be created from a product of experts. We learn the parameters of the mapping using gradient descent on the negative logarithm of the probability density function of the data under the model. We show that the mapping, though retaining its product of experts form, becomes more like a mixture of experts during training.

We present a supervised learning algorithm for estimation of generic input-output relations in a real-time, online fashion. The proposed method is based on a generalized expectation-maximization approach to fit an infinite mixture of linear experts (IMLE) to an online stream of data samples. This probabilistic model, while not fully Bayesian, can efficiently choose the number of experts that ar...

Generative feature spaces provide an elegant way to apply discriminative models in speech recognition, and system performance has been improved by adapting this framework. However, the classes in the feature space may be not linearly separable. Applying a linear classifier then limits performance. Instead of a single classifier, this paper applies a mixture of experts. This model trains differe...

We evaluate the performance of a heterogeneous mixture of neural network algorithms for predicting the exchange-traded fund DIA. A genetic algorithm is utilized to find the best mixture of neural networks, the topology of individual networks in the ensemble, and to determine the features set. The genetic algorithm also determines the window size of the input time-series supplied to the individu...