Results 241 to 250 of about 1,506,457 (286)

Use of spectroscopic methods in combination with linear discriminant analysis for authentication of food products

Food Control, 2018
Spectroscopic methods are efficient tools for food authentication due to the advantages of high sensitivity, rapidness, simplicity and their convenience.
M. Esteki, Z. Shahsavari, J. Simal-gandara   +2 more
semanticscholar   +1 more source

DDoS discrimination by Linear Discriminant Analysis (LDA)

2012 International Conference on Computing, Networking and Communications (ICNC), 2012
In this paper, we propose an effective approach with a supervised learning system based on Linear Discriminant Analysis (LDA) to discriminate legitimate traffic from DDoS attack traffic. Currently there is a wide outbreak of DDoS attacks that remain risky for the entire Internet.
Theerasak Thapngam, Shui Yu 0001, Wanlei Zhou 0001   +2 more
openaire   +1 more source

Linear Discriminant Analysis for Prediction of Group Membership: A User-Friendly Primer

Advances in Methods and Practices in Psychological Science, 2019
In psychology, researchers are often interested in the predictive classification of individuals. Various models exist for such a purpose, but which model is considered a best practice is conditional on attributes of the data.
Peter Boedeker, Nathan T. Kearns
semanticscholar   +1 more source

Training Linear Discriminant Analysis in Linear Time

2008 IEEE 24th International Conference on Data Engineering, 2008
Linear Discriminant Analysis (LDA) has been a popular method for extracting features which preserve class separability. It has been widely used in many fields of information processing, such as machine learning, data mining, information retrieval, and pattern recognition. However, the computation of LDA involves dense matrices eigen-decomposition which
Deng Cai 0001, Xiaofei He 0001, Jiawei Han 0001   +2 more
openaire   +1 more source

Conditional Linear Discriminant Analysis

18th International Conference on Pattern Recognition (ICPR'06), 2006
Dimensionality reduction by means of linear discriminant analysis (LDA) can generally lead to considerable improvements in classification accuracy and computation time. However, in supervised, pixel-based, image segmentation, the limiting factor of LDA that it cannot extract more than K - 1 features (K the number of classes) often prevents successfully
openaire   +1 more source

Unequal Priors in Linear Discriminant Analysis

Journal of Classification, 2019
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Carmen van Meegen, Sarah Schnackenberg, Uwe Ligges   +2 more
openaire   +1 more source

Boosting in Linear Discriminant Analysis

2000
In recent years, together with bagging [5] and the random subspace method [15], boosting [6] became one of the most popular combining techniques that allows us to improve a weak classifier. Usually, boosting is applied to Decision Trees (DT's). In this paper, we study boosting in Linear Discriminant Analysis (LDA).
Marina Skurichina, Robert P. W. Duin
openaire   +1 more source

Disambiguation Enabled Linear Discriminant Analysis for Partial Label Dimensionality Reduction

Knowledge Discovery and Data Mining, 2019
Partial label learning is an emerging weakly-supervised learning framework where each training example is associated with multiple candidate labels among which only one is valid.
Jing-Han Wu, Min-Ling Zhang
semanticscholar   +1 more source

Unsupervised Linear Discriminant Analysis for Jointly Clustering and Subspace Learning

IEEE Transactions on Knowledge and Data Engineering, 2019
Linear discriminant analysis (LDA) is one of commonly used supervised subspace learning methods. However, LDA will be powerless faced with the no-label situation.
Fei Wang, Quan Wang, F. Nie, Zhongheng Li, Weizhong Yu, Rong Wang   +5 more
semanticscholar   +1 more source

Bayes Optimality in Linear Discriminant Analysis

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008
We present an algorithm which provides the one-dimensional subspace where the Bayes error is minimized for the C class problem with homoscedastic Gaussian distributions. Our main result shows that the set of possible one-dimensional spaces v, for which the order of the projected class means is identical, defines a convex region with associated convex ...
Onur C. Hamsici, Aleix M. Martínez
openaire   +2 more sources