Results 131 to 140 of about 9,834 (275)

Multipose Face Recognition-Based Combined Adaptive Deep Learning Vector Quantization. [PDF]

Comput Intell Neurosci, 2020
Sarhan S, Nasr AA, Shams MY.
europepmc   +1 more source

Patch Processing for Relational Learning Vector Quantization

, 2012
Zhu X, Schleif F-M, Hammer B. Patch Processing for Relational Learning Vector Quantization. In: ISNN (1).
Frank-Michael Schleif, Hammer, Barbara ; https://orcid.org/, Xibin Zhu, Zhu, Xibin, Schleif, Frank-Michael, Schleif, Frank-Michael ; https://orcid.org/, Barbara Hammer, Hammer, Barbara   +7 more
core   +1 more source

RecLVQ: Recurrent Learning Vector Quantization

ESANN 2021 proceedings, 2021
Jensun Ravichandran, Thomas Villmann, Marika Kaden   +2 more
openaire   +1 more source

Baselining Large Language Model Performance in Systems Engineering Using SysEngBench

Systems Engineering, EarlyView.
ABSTRACT In the rapidly evolving field of artificial intelligence (AI), large language model s (LLMs) have demonstrated impressive capabilities in generating natural language. However, their proficiency in specialized domains, particularly in the field of systems engineering (SE), remains less explored and unquantified.
Ryan Bell, Ryan Longshore, Raymond Madachy, Ashlyn Hanrahan   +3 more
wiley   +1 more source

Learning Vector Quantization with Training Count (LVQTC)

, 1995
Kohonen's Learning Vector Quantization (LVQ) is modified by attributing training counters to each neuron, which record its training statistics. During training that allows for dynamic self-allocation of the neurons to classes.
Roberto Odorico
core  

DQN‐Guided Subset‐Induced OCSVM Kernel Approximation for Imbalanced Anomaly Detection

IEEJ Transactions on Electrical and Electronic Engineering, EarlyView.
Anomaly detection under limited normal data remains a fundamental challenge due to severe class imbalance and scarcity of anomalies. We propose a novel framework that reformulates support vector selection in One‐Class SVM as a sequential decision‐making problem.
Wenqian Yu, Jiaying Wu, Jinglu Hu
wiley   +1 more source

Stationarity of Matrix Relevance Learning Vector Quantization

, 2009
Biehl M, Hammer B, Schleif F-M, Schneider P, Villmann T. Stationarity of Matrix Relevance Learning Vector Quantization. Machine Learning Reports.
Hammer, Barbara ; https://orcid.org/, Biehl, M., Schleif, Frank-Michael ; https://orcid.org/, Villmann, T., Schneider, P.   +4 more
core  

Robust steganographic framework for securing sensitive healthcare data of telemedicine using convolutional neural network

CAAI Transactions on Intelligence Technology, EarlyView.
Abstract Data is the key element that runs the modern society. Large amounts of data are being released day by day as a result of many activities. The digital data is transferred through the Internet which may be vulnerable to attacks while transmitting. Especially, the medical data is observed to be of at most importance.
Rupa Ch, Venkayya Yadlapalli, Suhana Sulthana Sk, G. Thippa Reddy, Sandeep Kautish   +4 more
wiley   +1 more source

Using an Optimized Learning Vector Quantization- (LVQ-) Based Neural Network in Accounting Fraud Recognition.

Comput Intell Neurosci, 2021
Zheng Y, Ye X, Wu T.
europepmc   +1 more source

Dynamic Resource Allocation Optimisation and Security‐Resilient Control for Bandwidth‐Limited Network Control Systems With Data Conflicts

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT Networked control systems (NCSs) often suffer from performance degradation due to limited communication bandwidth, which can cause data transmission conflicts and packet loss. Existing scheduling strategies may fail to simultaneously meet the real‐time requirements and the importance of multisensor data, and they are particularly vulnerable ...
Da Chen, Kaibo Shi, Bin Guo, Kangkang Sun, Huaicheng Yan, Xiao Cai   +5 more
wiley   +1 more source