Review of deep learning algorithms and architectures
Deep learning (DL) is playing an increasingly important role in our lives. It has already made
a huge impact in areas, such as cancer diagnosis, precision medicine, self-driving cars …
a huge impact in areas, such as cancer diagnosis, precision medicine, self-driving cars …
Evolutionary large-scale multi-objective optimization: A survey
Multi-objective evolutionary algorithms (MOEAs) have shown promising performance in
solving various optimization problems, but their performance may deteriorate drastically …
solving various optimization problems, but their performance may deteriorate drastically …
Broad learning system: An effective and efficient incremental learning system without the need for deep architecture
CLP Chen, Z Liu - IEEE transactions on neural networks and …, 2017 - ieeexplore.ieee.org
Broad Learning System (BLS) that aims to offer an alternative way of learning in deep
structure is proposed in this paper. Deep structure and learning suffer from a time …
structure is proposed in this paper. Deep structure and learning suffer from a time …
Deep learning in microscopy image analysis: A survey
Computerized microscopy image analysis plays an important role in computer aided
diagnosis and prognosis. Machine learning techniques have powered many aspects of …
diagnosis and prognosis. Machine learning techniques have powered many aspects of …
A deep convolutional coupling network for change detection based on heterogeneous optical and radar images
We propose an unsupervised deep convolutional coupling network for change detection
based on two heterogeneous images acquired by optical sensors and radars on different …
based on two heterogeneous images acquired by optical sensors and radars on different …
Multiobjective evolution of fuzzy rough neural network via distributed parallelism for stock prediction
B Cao, J Zhao, Z Lv, Y Gu, P Yang… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Fuzzy rough theory can describe real-world situations in a mathematically effective and
interpretable way, while evolutionary neural networks can be utilized to solve complex …
interpretable way, while evolutionary neural networks can be utilized to solve complex …
A survey on evolutionary computation for computer vision and image analysis: Past, present, and future trends
Computer vision (CV) is a big and important field in artificial intelligence covering a wide
range of applications. Image analysis is a major task in CV aiming to extract, analyze and …
range of applications. Image analysis is a major task in CV aiming to extract, analyze and …
A review of the autoencoder and its variants: A comparative perspective from target recognition in synthetic-aperture radar images
In recent years, unsupervised feature learning based on a neural network architecture has
become a hot new topic for research [1]-[4]. The revival of interest in such deep networks can …
become a hot new topic for research [1]-[4]. The revival of interest in such deep networks can …
A survey of swarm and evolutionary computing approaches for deep learning
Deep learning (DL) has become an important machine learning approach that has been
widely successful in many applications. Currently, DL is one of the best methods of …
widely successful in many applications. Currently, DL is one of the best methods of …
Feature learning and change feature classification based on deep learning for ternary change detection in SAR images
Ternary change detection aims to detect changes and group the changes into positive
change and negative change. It is of great significance in the joint interpretation of spatial …
change and negative change. It is of great significance in the joint interpretation of spatial …