TABLE OF CONTENTS Preface

By P. Lin, J. Sinclair, H. Wiegand, George A. Bekey, Keith Abney, R. Allsopp, James M. Arthurs, Arthur J. Bronstein, L. Dagenais, R. London and 19 more. 2003

4,124 citations recorded by the source corpus.

Subfields: Astronomy and Astrophysics, Cognitive Neuroscience, Safety Research

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Citations in the corpus

Kernel Sparse Representation-Based Classifier (2012, methodology): “(NN) [12] and nearest subspace (NS) [20], [21], and compares”
Automatic Video Classification: A Survey of the Literature (2008, background): “An HMM represents a set of states and the probabilities of making a transition from one state to another state [12].”
NIRS Detection of Moldy Core in Apples (2010, methodology): “Classification was done by means of the maximum likelihood method (Duda et al. 2001): one third of the data were used for training the classifier; the remainder was used for cross-validation.”
Similarity Scores Based on Background Samples (2009, background): “Two matrices are then considered [23], SB measuring the covariance of the class centers, and SW , which is the sum of the covariance matrices of each class.”
Adaptive boosting for SAR automatic target recognition (2007, methodology): “PCA is one of the most commonly used dimensionality reduction method that projects pattern vectors onto a low-dimensional subspace, so that the reconstruction error is minimum in the least-square sense.”
Identifying protein interaction subnetworks by a bagging Markov random field-based method (2012, background): “From Equation (2), we can see that lower energies correspond to more likely configurations.”
The Strategic Robot Problem: Lethal Autonomous Weapons in War (2014, background): “While Lin et al. (2008) disagree with this point, as well as Michael Schmitt (2013), this is not impossible.”
Prototype-based models in machine learning. (2016, methodology): “Before discussing LVQ, we provide an intuitive introduction in terms of the simplest and most popular data-driven classifier: the Nearest neighbor (NN) classifier or its extensions to k-nearest neighbor (kNN) schemes.(3,6,50) It is roughly the machine learning counterpart of the idea of exemplars us”
Fast CU Splitting and Pruning for Suboptimal CU Partitioning in HEVC Intra Coding (2013, methodology): “The mean and variance of collected JLRD and JFRD are then estimated based on the maximum likelihood estimation (MLE) method [17], and the prior of each CU class is also computed based on the tallied occurrences of the nonsplit and split CUs at the end of the online update phase.”
Classification of Electrocardiogram Signals With Support Vector Machines and Particle Swarm Optimization (2008, methodology): “1 illustrates the distribution of the six considered classes drawn by means of 25 samples randomly selected for each class and the two best features according to the principal component analysis (PCA) algorithm [24].”