Results 91 to 100 of about 9,834 (275)

Resource‐Aware Contrastive Scattering Meta‐Learning for Efficient Few‐Shot Acoustic Anomaly Detection

Advanced Intelligent Systems, EarlyView.
This paper introduces a resource‐aware Contrastive Scattering Meta‐Learning (CSML) framework for acoustic anomaly detection. By leveraging training‐free wavelet scattering and metric‐based meta‐learning, the model achieves competitive performance with only 50 K learnable parameters—a 98% reduction compared to state‐of‐the‐art frameworks—enabling ...
Rami Zewail, Bassem Mokhtar
wiley   +1 more source

Retracted: Using an Optimized Learning Vector Quantization- (LVQ-) Based Neural Network in Accounting Fraud Recognition. [PDF]

Comput Intell Neurosci, 2023
Intelligence And Neuroscience C.
europepmc   +1 more source

Functional representation of prototypes in LVQ and relevance learning

, 2022
S.317-327We present a framework for distance-based classification of functional data. We consider the analysis of labeled spectral data by means of Generalized Matrix Relevance Learning Vector Quantization (GMLVQ) as an example.
Melchert, F., Biehl, M., Seiffert, U.
core   +1 more source

Visual and Visual‐Inertial SLAM Based on Enhanced Deep Learning Features and Motion Smoothness Constraints

Advanced Intelligent Systems, EarlyView.
A visual and visual‐inertial simultaneous localization and mapping (SLAM) algorithm, leveraging enhanced deep learning features and motion smoothness constraints, is proposed in this research work. This method retains the advantages of geometry‐based SLAM methods while effectively utilizing the powerful representational capabilities of data‐driven ...
Maosheng Jiang, Lei Ma, Muhua Zhang, Kai Shen   +3 more
wiley   +1 more source

Electroencephalography (EEG) eye state classification using learning vector quantization and bagged trees. [PDF]

Heliyon, 2023
Nilashi M, Abumalloh RA, Ahmadi H, Samad S, Alghamdi A, Alrizq M, Alyami S, Nayer FK.   +7 more
europepmc   +1 more source

Soft Learning Vector Quantization

, 2002
Learning Vector Quantization is a popular class of adaptive nearest prototype classifiers for multiclass classification, but learning algorithms from this family have so far been proposed on heuristic grounds.
Klaus Obermayer, Sambu Seo
core  

Time‐Delayed Spiking Reservoir Computing Enables Efficient Time Series Prediction

Advanced Intelligent Systems, EarlyView.
This study proposes time‐delayed spiking reservoir computing (TDSRC) for efficient time series prediction. By concatenating time‐lagged states, TDSRC constructs an expanded readout feature vector without altering internal reservoir dynamics. This approach enables highly accurate forecasting with significantly fewer neurons, providing a resource ...
Pin Jin, Ren Cao, Zhongxue Gan, Siyang Leng   +3 more
wiley   +1 more source

Medical diagnosis using artificial neural networks

Mathematics in Applied Sciences and Engineering
Medical diagnosis using Artificial Neural Networks (ANN) and computer-aided diagnosis with deep learning is currently a very active research area in medical science.
Afsana Begum, Md. Masiur Rahman, Sohana Jahan   +2 more
doaj   +1 more source

Penerapan Learning Vector Quantization (LVQ) untuk Klasifikasi Status Gizi Anak

IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 2013
Abstrak Penentuan klasifikasi status gizi anak yang sering dilakukan adalah berdasarkan indeks berat badan menurut tinggi badan (BB/TB). Pada Puskesmas Batupanjang, indeks antropometri tersebut dihitung secara manual untuk menilai status gizi anak ...
Elvia Budianita, Widodo Prijodiprodjo
doaj   +1 more source

Average Competitive Learning Vector Quantization

, 2012
We propose a new algorithm for vector quantization:Average Competitive Learning Vector Quantization(ACLVQ). It is a rather simple modi cation of the classical Competitive Learning Vector Quantization(CLVQ).
Salomon, Luis Armando, Lozada Chang, Li-Vang, Fort, Jean-Claude   +2 more
core