Results 51 to 60 of about 806,396 (338)
Advanced Materials Interfaces, EarlyView., 2023 This review discusses the use of Surface‐Enhanced Raman Spectroscopy (SERS) combined with Artificial Intelligence (AI) for detecting antimicrobial resistance (AMR). Various SERS studies used with AI techniques, including machine learning and deep learning, are analyzed for their advantages and limitations.
Zakarya Al‐Shaebi +4 more
wiley +1 more source
Exact heat kernel on a hypersphere and its applications in kernel SVM
, 2017 Many contemporary statistical learning methods assume a Euclidean feature space. This paper presents a method for defining similarity based on hyperspherical geometry and shows that it often improves the performance of support vector machine compared to ...
Song, Jun S., Zhao, Chenchao
core +1 more source
International Journal of Adaptive Control and Signal Processing, EarlyView.Summary Data‐driven forecasting of ship motions in waves is investigated through feedforward and recurrent neural networks as well as dynamic mode decomposition. The goal is to predict future ship motion variables based on past data collected on the field, using equation‐free approaches.
Matteo Diez +2 more
wiley +1 more source
Advanced Engineering Materials, EarlyView.In this study, the mechanical response of Y‐shaped core sandwich beams under compressive loading is investigated, using deep feed‐forward neural networks (DFNNs) for predictive modeling. The DFNN model accurately captures stress–strain behavior, influenced by design parameters and loading rates.
Ali Khalvandi +4 more
wiley +1 more source
In defense of local descriptor-based few-shot object detection
Frontiers in NeuroscienceState-of-the-art image object detection computational models require an intensive parameter fine-tuning stage (using deep convolution network, etc). with tens or hundreds of training examples.
Shichao Zhou +3 more
doaj +1 more source
Advanced Engineering Materials, EarlyView.Morphological features of three defect types in metal additive manufacturing (AM)—lack of fusion, keyhole, and gas‐entrapped pores—are statistically characterized using best‐fit distributions evaluated via coefficient‐of‐determination, Kolmogorov–Smirnov test, and quantile–quantile plots.
Ahmad Serjouei, Golnaz Shahtahmassebi
wiley +1 more source
, 2003 Bayesian methods allow for a simple and intuitive representation of the function spaces used by kernel methods. This chapter describes the basic principles of Gaussian Processes, their implementation and their connection to other kernel-based Bayesian estimation methods, such as the Relevance Vector Machine.
Smola, Alexander, Schoelkopf, Bernhard
openaire +3 more sources
Total stability of kernel methods [PDF]
Neurocomputing, 2018 Regularized empirical risk minimization using kernels and their corresponding reproducing kernel Hilbert spaces (RKHSs) plays an important role in machine learning. However, the actually used kernel often depends on one or on a few hyperparameters or the kernel is even data dependent in a much more complicated manner. Examples are Gaussian RBF kernels,
Dao-Hong Xiang +3 more
openaire +3 more sources
A distance-based kernel for classification via Support Vector Machines
Frontiers in Artificial IntelligenceSupport Vector Machines (SVMs) are a type of supervised machine learning algorithm widely used for classification tasks. In contrast to traditional methods that split the data into separate training and testing sets, here we propose an innovative ...
Nazhir Amaya-Tejera +3 more
doaj +1 more source
Efficient and Accurate Gaussian Image Filtering Using Running Sums
, 2011 This paper presents a simple and efficient method to convolve an image with a Gaussian kernel. The computation is performed in a constant number of operations per pixel using running sums along the image rows and columns.
Elboher, Elhanan, Werman, Michael
core +2 more sources