Cognitive Computation is an international, peer-reviewed, interdisciplinary journal that publishes cutting-edge articles describing original basic and applied work involving bio-inspired computational accounts of all aspects of natural and artificial cognitive systems. It provides a new platform for the dissemination of research, current practices and future trends in the emerging discipline of cognitive computation that bridges gap between life sciences, social sciences, engineering, physical and mathematical sciences, and humanities.
Cognitive Computation
Description
Identifiers
ISSN | 1866-9956 |
e-ISSN | 1866-9964 |
DOI | 10.1007/12559.1866-9964 |
Publisher
Springer US
Additional information
Data set: Springer
Articles
Cognitive Computation > 2019 > 11 > 6 > 778-788
Density models are fundamental in machine learning and have received a widespread application in practical cognitive modeling tasks and learning problems. In this work, we introduce a novel deep density model, referred to as deep mixtures of factor analyzers with common loadings (DMCFA), with an efficient greedy layer-wise unsupervised learning algorithm. The model employs a mixture of factor analyzers...
Cognitive Computation > 2019 > 11 > 6 > 869-878
Object transfiguration is a subtask of the image-to-image translation, which translates two independent image sets and has a wide range of applications. Recently, some studies based on Generative Adversarial Network (GAN) have achieved impressive results in the image-to-image translation. However, the object transfiguration task only translates regions containing target objects instead of whole images;...
Cognitive Computation > 2019 > 11 > 6 > 809-824
In an attempt to exploit the automatic feature extraction ability of biologically-inspired deep learning models, and enhance the learning of target features, we propose a novel deep learning algorithm. This is based on a deep convolutional neural network (DCNN) trained with an improved cost function, and combined with a support vector machine (SVM). Specifically, class separation information, which...