Recent research has shown great progress on fine-grained entity typing. Most existing methods require pre-defining a set of types and training a multi-class classifier from a large labeled data set based on multi-level linguistic features. They are thus limited to certain domains, genres and languages. In this paper, we propose a novel unsupervised entity typing framework by combining symbolic ...

image classification is an issue which utilizes image processing, pattern recognition and classification methods. automatic medical image classification is a progressive area in image classification and it expected to be more developed in the future. due to this fact that automatic diagnosis which use intelligent methods such as medical image classification can assist pathologists by providing ...

Federated learning has attracted increasing attention with the emergence of distributed data. While extensive federated algorithms have been proposed for non-convex problem, in practice still faces numerous challenges, such as large training iterations to converge since sizes models and datasets keep increasing, lack adaptivity by SGD-based model updates. Meanwhile, study adaptive methods is sc...

In this paper, a novel unsupervised competitive learning algorithm, called the centroid neural network adaptive resonance theory (CNN-ART) algorithm, is proposed to relieve the dependence on the initial codewords of the codebook in contrast to the conventional algorithms with vector quantization in lossy image compression. The design of the CNN-ART algorithm is mainly based on the adaptive reso...

To quantify the adaptive significance of insect learning, we documented the behavior and growth rate of grasshoppers (Schistocerca americana) in an environment containing two artificial food types, one providing a balanced diet of protein and carbohydrate, which maximizes growth, and the other being carbohydrate-deficient, which is unsuitable for growth. Grasshoppers in the Learning treatment e...

In recent years, researches on reinforcement learning (RL) have focused on bridging the gap between adaptive optimal control and bio-inspired learning techniques. Neural network reinforcement learning (NNRL) is among the most popular algorithms in the RL framework. The advantage of using neural networks enables the RL to search for optimal policies more efficiently in several real-life applicat...