Pattern Classification

By Shigeo Abe DrEng, Shigeo Abe. 2012

15,064 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Security Evaluation of Support Vector Machines in Adversarial Environments (2014, background): “3 A representation of the design steps of a machine learning system [29] that may provide sources of knowledge for the adversary.”
Using an Ensemble of One-Class SVM Classifiers to Harden Payload-based Anomaly Detection Systems (2006, methodology): “Larger values of n are impractical given the exponentially growing dimensionality of the feature space and the curse of dimensionality problem [9].”
Transportation mode detection using mobile phones and GIS information (2011, background): “Readers are referred to [32, 33] for discussions on various classification models.”
Robust Face Recognition via Sparse Representation (2009, background): “Conversely, the theory of sparse representation and compressed sensing yields new insights into two crucial issues in automatic face recognition: the role of feature extraction and the difﬁculty due to occlusion. a) The Role of Feature Extraction: The question of which low-dimensional features of…”
See all by looking at a few: Sparse modeling for finding representative objects (2012, methodology): “We evaluate the classification performance using several standard classification algorithms: Nearest Neighbor (NN) [9], Nearest Subspace (NS) [22], Sparse Representation-based Classification (SRC) [40], and Linear Support Vector Machine (SVM) [9].”
Face liveness detection with component dependent descriptor (2013, methodology): “Contributions of this paper are bellows: (1) we exploit not only the canonical face region as described in previous face-oriented works, but also some other informative regions, such as hair, cheek, etc; (2) Compared with the methods which uniformly divide image into grids, we propose a component-ba”
Learning sparse models for a dynamic Bayesian network classifier of protein secondary structure (2011, background): “This type of regularization is known as shrinkage in statistical machine learning [54].”
Subsequence-based feature map for protein function classification (2008, methodology): “…to find simiar sequences whose functions are experimentally determined. his is usually performed by searching public databases using ocal alignment search tools such as BLAST or PSI-BLAST and nnotations for the highest scoring hits are transferred onto the ew sequence (Altschul et al., 1990, 1997).”
Reanimating Faces in Images and Video (2003, background): “1: The algorithm fits the morphable face model to consecutive frames by minimizing image difference EI (Equation 5) and a regularization term EP (7) 3 27 25.”
Robot Odor Localization: A Taxonomy and Survey (2008, methodology): “The matrix A t represents the transition probabilities (of odor from one cell to another), represents the initial probabilities of each state, and b represents the detection probabilities, giving the HMM [ A t b] (see Alpaydin (2004) Duda et al. (2001) for detailed descriptions of HMMs).”
Defining transcription modules using large-scale gene expression data (2004, neutral): “In contrast to most clustering methods, where genes are grouped by optimizing all clusters simultaneously (Duda et al., 2001), our approach focuses on the properties of the individual co-regulated units themselves.”
Analysis of Emotionally Salient Aspects of Fundamental Frequency for Emotion Detection (2009, methodology): “For this purpose, we proposed the use of the K-means clustering algorithm to estimate the bins [34].”