@conference {12446,
	title = {Illumination robust dictionary-based face recognition},
	booktitle = {2011 18th IEEE International Conference on Image Processing (ICIP)},
	year = {2011},
	month = {2011/09/11/14},
	pages = {777 - 780},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {In this paper, we present a face recognition method based on simultaneous sparse approximations under varying illumination. Our method consists of two main stages. In the first stage, a dictionary is learned for each face class based on given training examples which minimizes the representation error with a sparseness constraint. In the second stage, a test image is projected onto the span of the atoms in each learned dictionary. The resulting residual vectors are then used for classification. Furthermore, to handle changes in lighting conditions, we use a relighting approach based on a non-stationary stochastic filter to generate multiple images of the same person with different lighting. As a result, our algorithm has the ability to recognize human faces with good accuracy even when only a single or a very few images are provided for training. The effectiveness of the proposed method is demonstrated on publicly available databases and it is shown that this method is efficient and can perform significantly better than many competitive face recognition algorithms.},
	keywords = {albedo, approximation theory, classification, competitive face recognition algorithms, Databases, Dictionaries, Face, face recognition, face recognition method, filtering theory, human face recognition, illumination robust dictionary-based face recognition, illumination variation, image representation, learned dictionary, learning (artificial intelligence), lighting, lighting conditions, multiple images, nonstationary stochastic filter, publicly available databases, relighting, relighting approach, representation error, residual vectors, Robustness, simultaneous sparse approximations, simultaneous sparse signal representation, sparseness constraint, Training, varying illumination, vectors},
	isbn = {978-1-4577-1304-0},
	doi = {10.1109/ICIP.2011.6116670},
	author = {Patel, Vishal M. and Tao Wu and Biswas,S. and Phillips,P.J. and Chellapa, Rama}
}
@conference {14850,
	title = {Trainable 3D recognition using stereo matching},
	booktitle = {Computer Vision Workshops (ICCV Workshops), 2011 IEEE International Conference on},
	year = {2011},
	month = {2011///},
	pages = {625 - 631},
	abstract = {Stereo matching has been used for face recognition in the presence of pose variation. In this approach, stereo matching is used to compare two 2-D images based on correspondences that reflect the effects of viewpoint variation and allow for occlusion. We show how to use stereo matching to derive image descriptors that can be used to train a classifier. This improves face recognition performance, producing the best published results on the CMU PIE dataset. We also demonstrate that classification based on stereo matching can be used for general object classification in the presence of pose variation. In preliminary experiments we show promising results on the 3D object class dataset, a standard, challenging 3D classification data set.},
	keywords = {2D, 3D, class, classification, classification;image, data, dataset;CMU, dataset;face, descriptor;occlusion;pose, estimation;solid, image, image;3D, matching;pose, matching;trainable, modelling;stereo, object, PIE, processing;, recognition;face, recognition;image, set;3D, variation;stereo},
	doi = {10.1109/ICCVW.2011.6130301},
	author = {Castillo,C. D and Jacobs, David W.}
}
@conference {14451,
	title = {Identifying graphs from noisy and incomplete data},
	booktitle = {Proceedings of the 1st ACM SIGKDD Workshop on Knowledge Discovery from Uncertain Data},
	series = {U {\textquoteright}09},
	year = {2009},
	month = {2009///},
	pages = {23 - 29},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {There is a growing wealth of data describing networks of various types, including social networks, physical networks such as transportation or communication networks, and biological networks. At the same time, there is a growing interest in analyzing these networks, in order to uncover general laws that govern their structure and evolution, and patterns and predictive models to develop better policies and practices. However, a fundamental challenge in dealing with this newly available observational data describing networks is that the data is often of dubious quality -- it is noisy and incomplete -- and before any analysis method can be applied, the data must be cleaned, and missing information inferred. In this paper, we introduce the notion of graph identification, which explicitly models the inference of a "cleaned" output network from a noisy input graph. It is this output network that is appropriate for further analysis. We present an illustrative example and use the example to explore the types of inferences involved in graph identification, as well as the challenges and issues involved in combining those inferences. We then present a simple, general approach to combining the inferences in graph identification and experimentally show the utility of our combined approach and how the performance of graph identification is sensitive to the inter-dependencies among these inferences.},
	keywords = {classification, data mining, entity resolution, link prediction, social networks, statistical relational learning},
	isbn = {978-1-60558-675-5},
	doi = {10.1145/1610555.1610559},
	url = {http://doi.acm.org/10.1145/1610555.1610559},
	author = {Namata,Jr.,Galileo Mark S. and Getoor, Lise}
}
@conference {17007,
	title = {Categorizing web search results into meaningful and stable categories using fast-feature techniques},
	booktitle = {Proceedings of the 6th ACM/IEEE-CS joint conference on Digital libraries},
	series = {JCDL {\textquoteright}06},
	year = {2006},
	month = {2006///},
	pages = {210 - 219},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {When search results against digital libraries and web resources have limited metadata, augmenting them with meaningful and stable category information can enable better overviews and support user exploration. This paper proposes six fast-feature techniques that use only features available in the search result list, such as title, snippet, and URL, to categorize results into meaningful categories. They use credible knowledge resources, including a US government organizational hierarchy, a thematic hierarchy from the Open Directory Project (ODP) web directory, and personal browse histories, to add valuable metadata to search results. In three tests the percent of results categorized for five representative queries was high enough to suggest practical benefits: general web search (76-90\%), government web search (39-100\%), and the Bureau of Labor Statistics website (48-94\%). An additional test submitted 250 TREC queries to a search engine and successfully categorized 66\% of the top 100 using the ODP and 61\% of the top 350. Fast-feature techniques have been implemented in a prototype search engine. We propose research directions to improve categorization rates and make suggestions about how web site designers could re-organize their sites to support fast categorization of search results.},
	keywords = {Browsing, categorization, classification, metadata, open directory, taxonomies},
	isbn = {1-59593-354-9},
	doi = {10.1145/1141753.1141801},
	url = {http://doi.acm.org/10.1145/1141753.1141801},
	author = {Kules,Bill and Kustanowitz,Jack and Shneiderman, Ben}
}
@conference {12693,
	title = {Classification probability analysis of principal component space analysis},
	booktitle = {Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on},
	volume = {1},
	year = {2004},
	month = {2004/08//},
	pages = {240 - 243 Vol.1 - 240 - 243 Vol.1},
	abstract = {In a previous paper, we have presented a new linear classification algorithm, principal component space analysis (PCNSA) which is designed for problems like object recognition where different classes have unequal and non-white noise covariance matrices. PCNSA first obtains a principal components space (PCA space) for the entire data and in this PCA space, it finds for each class "i", an M<sub>i</sub> dimensional subspace along which the class{\textquoteright}s intra-class variance is the smallest. We call this subspace an approximate space (ANS) since the lowest variance is usually "much smaller" than the highest. A query is classified into class "i" if its distance from the class{\textquoteright}s mean in the class{\textquoteright}s ANS is a minimum. In this paper, we discuss the PCNSA algorithm more precisely and derive tight upper bounds on its classification error probability. We use these expressions to compare classification performance of PCNSA with that of subspace linear discriminant analysis (SLDA).},
	keywords = {algorithm;, analysis;, approximate, classification, classification;, component, covariance, discriminant, error, intraclass, linear, matrices;, matrix;, noise;, nonwhite, object, pattern, PCA;, principal, probability;, recognition;, space, space;, statistics;, subspace, variance;},
	doi = {10.1109/ICPR.2004.1334068},
	author = {Vaswani, N. and Chellapa, Rama}
}
@conference {18400,
	title = {Maintaining software with a security perspective},
	booktitle = {Software Maintenance, 2002. Proceedings. International Conference on},
	year = {2002},
	month = {2002///},
	pages = {194 - 203},
	abstract = {Testing for software security is a lengthy, complex and costly process. Currently, security testing is done using penetration analysis and formal verification of security kernels. These methods are not complete and are difficult to use. Hence it is essential to focus testing effort in areas that have a greater number of security vulnerabilities to develop secure software as well as meet budget and time constraints. We propose a testing strategy based on a classification of vulnerabilities to develop secure and stable systems. This taxonomy will enable a system testing and maintenance group to understand the distribution of security vulnerabilities and prioritize their testing effort according to the impact the vulnerabilities have on the system. This is based on Landwehr{\textquoteright}s (1994) classification scheme for security flaws and we evaluated it using a database of 1360 operating system vulnerabilities. This analysis indicates vulnerabilities tend to be focused in relatively few areas and associated with a small number of software engineering issues.},
	keywords = {(computers);, budget, classification, classification;, constraints;, data;, engineering;, flaw, maintenance;, of, operating, program, scheme;, Security, software, stable, system, systems, systems;, testing;, TIME, vulnerabilities;, vulnerability},
	doi = {10.1109/ICSM.2002.1167766},
	author = {Jiwnani,K. and Zelkowitz, Marvin V}
}
@article {13737,
	title = {Chinese-English Semantic Resource Construction},
	year = {2000},
	month = {2000/06//},
	institution = {Instititue for Advanced Computer Studies, Univ of Maryland, College Park},
	abstract = {We describe an approach to large-scale construction of a semantic lexicon for Chinese verbs. We leverage off of three existing resources-- a classification of English verbs called EVCA (English Verbs Classes and Alternations), a Chinese conceptual database called HowNet, and a large-machine readable dictionary called Optilex. The resulting lexicon is used for determining appropriate word senses in applications such as machine translation and cross-language information retrieval.},
	keywords = {*CHINESE LANGUAGE, *ENGLISH LANGUAGE, *LEXICONS, *SEMANTICS, *VERBS, *WORD SENSES, *WORDS(LANGUAGE), classification, CONCEPTS, CONSTRUCTION, Dictionaries, INFORMATION SCIENCE, linguistics, VERB CLASSES, Vocabulary},
	url = {http://stinet.dtic.mil/oai/oai?\&verb=getRecord\&metadataPrefix=html\&identifier=ADA458703},
	author = {Dorr, Bonnie J and Levow,Gina-Anne and Lin,Dekang and Thomas,Scott}
}
@conference {18373,
	title = {An improved classification tree analysis of high cost modules based upon an axiomatic definition of complexity},
	booktitle = {Software Reliability Engineering, 1992. Proceedings., Third International Symposium on},
	year = {1992},
	month = {1992/10//},
	pages = {164 - 172},
	abstract = {Identification of high cost modules has been viewed as one mechanism to improve overall system reliability, since such modules tend to produce more than their fair share of problems. A decision tree model has previously been used to identify such modules. In this paper, a previously developed axiomatic model of program complexity is merged with the previously developed decision tree process for an improvement in the ability to identify such modules. This improvement has been tested using data from the NASA Software Engineering Laboratory},
	keywords = {(mathematics);, analysis;, axiomatic, classification, classification;, complexity;, computational, cost, decision, definition;, high, metrics;, model;, modules;, overall, program, reliability;, software, subroutines;, system, tree, TREES},
	doi = {10.1109/ISSRE.1992.285848},
	author = {Tian,Jianhui and Porter, Adam and Zelkowitz, Marvin V}
}
@conference {16320,
	title = {Software metric classification trees help guide the maintenance of large-scale systems},
	booktitle = {, Conference on Software Maintenance, 1989., Proceedings},
	year = {1989},
	month = {1989/10/16/19},
	pages = {116 - 123},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {The 80:20 rule states that approximately 20\% of a software system is responsible for 80\% of its errors. The authors propose an automated method for generating empirically-based models of error-prone software objects. These models are intended to help localize the troublesome 20\%. The method uses a recursive algorithm to automatically generate classification trees whose nodes are multivalued functions based on software metrics. The purpose of the classification trees is to identify components that are likely to be error prone or costly, so that developers can focus their resources accordingly. A feasibility study was conducted using 16 NASA projects. On average, the classification trees correctly identified 79.3\% of the software modules that had high development effort or faults},
	keywords = {automated method, automatic programming, classification, Classification tree analysis, classification trees, Computer errors, empirically-based models, error-prone software objects, Fault diagnosis, feasibility study, high development effort, Large-scale systems, multivalued functions, NASA, NASA projects, recursive algorithm, Software algorithms, software engineering, Software maintenance, Software measurement, software metrics, software modules, Software systems, trees (mathematics)},
	isbn = {0-8186-1965-1},
	doi = {10.1109/ICSM.1989.65202},
	author = {Selby,R. W and Porter, Adam}
}
