[HTML][HTML] Data science applications to string theory
F Ruehle - Physics Reports, 2020 - Elsevier
We first introduce various algorithms and techniques for machine learning and data science.
While there is a strong focus on neural network applications in unsupervised, supervised …
While there is a strong focus on neural network applications in unsupervised, supervised …
[HTML][HTML] A path toward explainable AI and autonomous adaptive intelligence: deep learning, adaptive resonance, and models of perception, emotion, and action
S Grossberg - Frontiers in neurorobotics, 2020 - frontiersin.org
Biological neural network models whereby brains make minds help to understand
autonomous adaptive intelligence. This article summarizes why the dynamics and emergent …
autonomous adaptive intelligence. This article summarizes why the dynamics and emergent …
[HTML][HTML] 40 years of cognitive architectures: core cognitive abilities and practical applications
I Kotseruba, JK Tsotsos - Artificial Intelligence Review, 2020 - Springer
In this paper we present a broad overview of the last 40 years of research on cognitive
architectures. To date, the number of existing architectures has reached several hundred …
architectures. To date, the number of existing architectures has reached several hundred …
[HTML][HTML] Improving malicious URLs detection via feature engineering: Linear and nonlinear space transformation methods
In malicious URLs detection, traditional classifiers are challenged because the data volume
is huge, patterns are changing over time, and the correlations among features are …
is huge, patterns are changing over time, and the correlations among features are …
Lifelong machine learning with deep streaming linear discriminant analysis
When an agent acquires new information, ideally it would immediately be capable of using
that information to understand its environment. This is not possible using conventional deep …
that information to understand its environment. This is not possible using conventional deep …
[HTML][HTML] Efficient treatment of outliers and class imbalance for diabetes prediction
N Nnamoko, I Korkontzelos - Artificial intelligence in medicine, 2020 - Elsevier
Learning from outliers and imbalanced data remains one of the major difficulties for machine
learning classifiers. Among the numerous techniques dedicated to tackle this problem, data …
learning classifiers. Among the numerous techniques dedicated to tackle this problem, data …
[图书][B] Handbook of neural computation
E Fiesler, R Beale - 2020 - books.google.com
The Handbook of Neural Computation is a practical, hands-on guide to the design and
implementation of neural networks used by scientists and engineers to tackle difficult and/or …
implementation of neural networks used by scientists and engineers to tackle difficult and/or …
Literature review on transfer learning for human activity recognition using mobile and wearable devices with environmental technology
Activity recognition systems utilise data from sensors in mobile, environmental and wearable
devices, ubiquitously available to individuals. It is a growing research area within intelligent …
devices, ubiquitously available to individuals. It is a growing research area within intelligent …
Neural memory plasticity for medical anomaly detection
In the domain of machine learning, Neural Memory Networks (NMNs) have recently
achieved impressive results in a variety of application areas including visual question …
achieved impressive results in a variety of application areas including visual question …
Short-term electricity price forecasting and classification in smart grids using optimized multikernel extreme learning machine
Short-term electricity price forecasting in deregulated electricity markets has been studied
extensively in recent years but without significant reduction in price forecasting errors. Also …
extensively in recent years but without significant reduction in price forecasting errors. Also …