Relevant bibliographies by topics / Linear Pattern Recognition

Academic literature on the topic 'Linear Pattern Recognition'

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Published: 9 March 2023
Last updated: 10 March 2023

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Journal articles on the topic "Linear Pattern Recognition"

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Ordowski, Mark, and Gerard G. L. Meyer. "Geometric linear discriminant analysis for pattern recognition." Pattern Recognition 37, no. 3 (March 2004): 421–28. http://dx.doi.org/10.1016/j.patcog.2003.07.002.

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Eremin, I. I., V. D. Mazurov, and N. N. Astaf 'ev. "Linear inequalities in mathematical programming and pattern recognition." Ukrainian Mathematical Journal 40, no. 3 (1989): 243–51. http://dx.doi.org/10.1007/bf01061299.

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Li, Yang, Jian-Hui Jiang, Zeng-Ping Chen, Cheng-Jian Xu, and Ru-Qin Yu. "Robust linear discriminant analysis for chemical pattern recognition." Journal of Chemometrics 13, no. 1 (January 1999): 3–13. http://dx.doi.org/10.1002/(sici)1099-128x(199901/02)13:1<3::aid-cem524>3.0.co;2-r.

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Wu, Wei, Wei Qi Yuan, and Sen Lin. "An Instrument of Palm Vein Pattern Recognition." Applied Mechanics and Materials 333-335 (July 2013): 1092–95. http://dx.doi.org/10.4028/www.scientific.net/amm.333-335.1092.

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Palm vein pattern recognition is one of the newest biometric techniques researched today. This paper presents a palm vein recognition instrument that uses blood vessel patterns as a personal identifying factor. The instrument uses the recognition algorithm of two dimensional Fisher linear discriminant for classification. The experiment has been done in a self-build palm vein database. Experimental results show that the designed instrument achieves an acceptable level of performance.
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Cooke, T. "Two variations on Fisher's linear discriminant for pattern recognition." IEEE Transactions on Pattern Analysis and Machine Intelligence 24, no. 2 (2002): 268–73. http://dx.doi.org/10.1109/34.982904.

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Nieves, Juan L., Javier Hernández-Andrés, Eva Valero, and Javier Romero. "Spectral-reflectance linear models for optical color-pattern recognition." Applied Optics 43, no. 9 (March 19, 2004): 1880. http://dx.doi.org/10.1364/ao.43.001880.

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Reed, Stuart, and Jeremy Coupland. "Cascaded linear shift-invariant processors in optical pattern recognition." Applied Optics 40, no. 23 (August 10, 2001): 3843. http://dx.doi.org/10.1364/ao.40.003843.

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Hosgurmath, Sangamesh, Viswanatha Vanjre Mallappa, Nagaraj B. Patil, and Vishwanath Petli. "A face recognition system using convolutional feature extraction with linear collaborative discriminant regression classification." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 2 (April 1, 2022): 1468. http://dx.doi.org/10.11591/ijece.v12i2.pp1468-1476.

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Face recognition is one of the important biometric authentication research areas for security purposes in many fields such as pattern recognition and image processing. However, the human face recognitions have the major problem in machine learning and deep learning techniques, since input images vary with poses of people, different lighting conditions, various expressions, ages as well as illumination conditions and it makes the face recognition process poor in accuracy. In the present research, the resolution of the image patches is reduced by the max pooling layer in convolutional neural network (CNN) and also used to make the model robust than other traditional feature extraction technique called local multiple pattern (LMP). The extracted features are fed into the linear collaborative discriminant regression classification (LCDRC) for final face recognition. Due to optimization using CNN in LCDRC, the distance ratio between the classes has maximized and the distance of the features inside the class reduces. The results stated that the CNN-LCDRC achieved 93.10% and 87.60% of mean recognition accuracy, where traditional LCDRC achieved 83.35% and 77.70% of mean recognition accuracy on ORL and YALE databases respectively for the training number 8 (i.e. 80% of training and 20% of testing data).
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Bobrowski, Leon. "Ranked linear models and sequential patterns recognition." Pattern Analysis and Applications 12, no. 1 (November 27, 2007): 1–7. http://dx.doi.org/10.1007/s10044-007-0092-8.

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Perez, C. A., G. D. Gonzalez, L. E. Medina, and F. J. Galdames. "Linear Versus Nonlinear Neural Modeling for 2-D Pattern Recognition." IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 35, no. 6 (November 2005): 955–64. http://dx.doi.org/10.1109/tsmca.2005.851268.

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Dissertations / Theses on the topic "Linear Pattern Recognition"

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Reed, Stuart. "Cascaded linear shift invariant processing in pattern recognition." Thesis, Loughborough University, 2000. https://dspace.lboro.ac.uk/2134/7481.

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Image recognition is the process of classifying a pattern in an image into one of a number of stored classes. It is used in such diverse applications as medical screening, quality control in manufacture and military target recognition. An image recognition system is called shift invariant if a shift of the pattern in the input image produces a proportional shift in the output, meaning that both the class and location of the object in the image are identified. The work presented in this thesis considers a cascade of linear shift invariant optical processors, or correlators, separated by fields of point non-lineari ties, called the cascaded correlator. This is introduced as a method of providing parallel, shiftinvariant, non-linear pattern recognition in a system that can learn in the manner of neural networks. It is shown that if a neural network is constrained to give overall shift invariance, the resulting structure is a cascade of correlators, meaning that the cascaded correlator is the only architecture which will provide fully shift invariant pattern recognition. The issues of training of such a non-linear system are discussed in neural network terms, and the non-linear decisions of the system are investigated. By considering digital simulations of a two-stage system, it is shown that the cascaded correlator is superior to linear filtering for both discrimination and tolerance to image distortion. This is shown for theoretical images and in real-world applications based on fault identification in can manufacture. The cascaded correlator has also been proven as an optical system by implementation in a joint transform correlator architecture. By comparing simulated and optical results, the resulting practical errors are analysed and compensated. It is shown that the optical implementation produces results similar to those of the simulated system, meaning that it is possible to provide a highly non-linear decision using robust parallel optical processing techniques.
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Lee, Richard. "3D non-linear image restoration algorithms." Thesis, University of East Anglia, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.338227.

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Wang, Jian. "Non-linear techniques for image processing." Thesis, King's College London (University of London), 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.336582.

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Ruan, Yang. "Smooth and locally linear semi-supervised metric learning /." View abstract or full-text, 2009. http://library.ust.hk/cgi/db/thesis.pl?CSED%202009%20RUAN.

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Powell, Heather M. "Impedance imaging using linear arrays of electrodes." Thesis, University of Sheffield, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.306500.

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Gonzalez, Adrian. "Spatial pattern recognition for crop-livestock systems using multispectral data." Thesis, University of Edinburgh, 2008. http://hdl.handle.net/1842/3790.

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Within the field of pattern recognition (PR) a very active area is the clustering and classification of multispectral data, which basically aims to allocate the right class of ground category to a reflectance or radiance signal. Generally, the problem complexity is related to the incorporation of spatial characteristics that are complementary to the nonlinearities of land surface process heterogeneity, remote sensing effects and multispectral features. The present research describes the application of learning machine methods to accomplish the above task by inducting a relationship between the spectral response of farms’ land cover, and their farming system typology from a representative set of instances. Such methodologies are not traditionally used in crop-livestock studies. Nevertheless, this study shows that its application leads to simple and theoretically robust classification models. The study has covered the following phases: a)geovisualization of crop-livestock systems; b)feature extraction of both multispectral and attributive data and; c)supervised farm classification. The first is a complementary methodology to represent the spatial feature intensity of farming systems in the geographical space. The second belongs to the unsupervised learning field, which mainly involves the appropriate description of input data in a lower dimensional space. The last is a method based on statistical learning theory, which has been successfully applied to supervised classification problems and to generate models described by implicit functions. In this research the performance of various kernel methods applied to the representation and classification of crop-livestock systems described by multispectral response is studied and compared. The data from those systems include linear and nonlinearly separable groups that were labelled using multidimensional attributive data. Geovisualization findings show the existence of two well-defined farm populations within the whole study area; and three subgroups in relation to the Guarico section. The existence of these groups was confirmed by both hierarchical and kernel clustering methods, and crop-livestock systems instances were segmented and labeled into farm typologies based on: a)milk and meat production; b)reproductive management; c)stocking rate; and d)crop-forage-forest land use. The minimum set of labeled examples to properly train the kernel machine was 20 instances. Models inducted by training data sets using kernel machines were in general terms better than those from hierarchical clustering methodologies. However, the size of the training data set represents one of the main difficulties to be overcome in permitting the more general application of this technique in farming system studies. These results attain important implications for large scale monitoring of crop-livestock system; particularly to the establishment of balanced policy decision, intervention plans formulation, and a proper description of target typologies to enable investment efforts to be more focused at local issues.
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Ma, Jinhua. "Dependency modeling for information fusion with applications in visual recognition." HKBU Institutional Repository, 2013. https://repository.hkbu.edu.hk/etd_ra/1522.

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Peacegood, Gillian. "A knowledge-based system for extraction and recognition of linear features in high resolution remotely-sensed imagery." Thesis, Kingston University, 1989. http://eprints.kingston.ac.uk/20529/.

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A knowledge-based system for the automatic extraction and recognition of linear features from digital imagery has been developed, with a knowledge base applied to the recognition of linear features in high resolution remotely sensed imagery, such as SPOT HRV and XS, Thematic Mapper and high altitude aerial photography. In contrast to many knowledge-based vision systems, emphasis is placed on uncertainty and the exploitation of context via statistical inferencing techniques, and issues of strategy and control are given less emphasis. Linear features are extracted from imagery, which may be multiband imagery, using an edge detection and tracking algorithm. A relational database for the representation of linear features has been developed, and this is shown to be useful in a number of applications, including general purpose query and display. A number of proximity relationships between the linear features in the database are established, using computationally efficient algorithms. Three techniques for classifying the linear features by exploiting uncertainty and context have been implemented and are compared. These are Bayesian inferencing using belief networks, a new inferencing technique based on belief functions and relaxation labelling using belief functions. The two inferencing techniques are shown to produce more realistic results than probabilistic relaxation, and the new inferericing method based on belief functions to perform best in practical situations. Overall, the system is shown to produce reasonably good classification results on hand extracted linear features, although the classification is less good on automatically extracted linear features because of shortcomings in the edge detection and extraction processes. The system adopts many of the features of expert systems, including complete separation of control from stored knowledge and justification for the conclusions reached.
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Sharma, Alok. "Linear Models for Dimensionality Reduction and Statistical Pattern Recognition for Supervised and Unsupervised Tasks." Thesis, Griffith University, 2006. http://hdl.handle.net/10072/365298.

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In this dissertation a number of novel algorithms for dimension reduction and statistical pattern recognition for both supervised and unsupervised learning tasks have been presented. Several existing pattern classifiers and dimension reduction algorithms are studied. Their limitations and/or weaknesses are considered and accordingly improved techniques are given which overcome several of their shortcomings. In particular, the following research works are carried out: • Literature survey of basic techniques for pattern classification like Gaussian mixture model (GMM), expectation-maximization (EM) algorithm, minimum distance classifier (MDC), vector quantization (VQ), nearest neighbour (NN) and k-nearest neighbour (kNN) are conducted. • Survey of basic dimensional reduction tools viz. principal component analysis (PCA) and linear discriminant analysis (LDA) are conducted. These techniques are also considered for pattern classification purposes. • Development of Fast PCA technique which finds the desired number of leading eigenvectors with much less computational cost and requires extremely low processing time as compared to the basic PCA model. • Development of gradient LDA technique which solves the small sample size problem as was not possible by basic LDA technique. • The rotational LDA technique is developed which efficiently reduces the overlapping of samples between the classes to a large extent as compared to the basic LDA technique. • A combined classifier using MDC, class-dependent PCA and LDA is designed which improves the performance of the classifier which was not possible by using single classifiers. The application of PCA prior to LDA is conducted in such a way that it avoids small sample size problem (if present). • The splitting technique initialization is introduced in the local PCA technique. The proposed integration enables easier data processing and more accurate representation of multivariate data. • A combined technique using VQ and vector quantized principal component analysis (VQPCA) is presented which provides significant improvement in the classifier performance (in terms of accuracy) at very low storage and processing time requirements compared to individual and several other classifiers. • Survey on unsupervised learning task like independent component analysis (ICA) is conducted. • A new perspective of subspace ICA (generalized ICA, where all the components need not be independent) is introduced by developing vector kurtosis (an extension of kurtosis) function.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
Griffith School of Engineering
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Wang, Xuechuan, and n/a. "Feature Extraction and Dimensionality Reduction in Pattern Recognition and Their Application in Speech Recognition." Griffith University. School of Microelectronic Engineering, 2003. http://www4.gu.edu.au:8080/adt-root/public/adt-QGU20030619.162803.

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Conventional pattern recognition systems have two components: feature analysis and pattern classification. Feature analysis is achieved in two steps: parameter extraction step and feature extraction step. In the parameter extraction step, information relevant for pattern classification is extracted from the input data in the form of parameter vector. In the feature extraction step, the parameter vector is transformed to a feature vector. Feature extraction can be conducted independently or jointly with either parameter extraction or classification. Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) are the two popular independent feature extraction algorithms. Both of them extract features by projecting the parameter vectors into a new feature space through a linear transformation matrix. But they optimize the transformation matrix with different intentions. PCA optimizes the transformation matrix by finding the largest variations in the original feature space. LDA pursues the largest ratio of between-class variation and within-class variation when projecting the original feature space to a subspace. The drawback of independent feature extraction algorithms is that their optimization criteria are different from the classifier’s minimum classification error criterion, which may cause inconsistency between feature extraction and the classification stages of a pattern recognizer and consequently, degrade the performance of classifiers. A direct way to overcome this problem is to conduct feature extraction and classification jointly with a consistent criterion. Minimum classification Error (MCE) training algorithm provides such an integrated framework. MCE algorithm was first proposed for optimizing classifiers. It is a type of discriminative learning algorithm but achieves minimum classification error directly. The flexibility of the framework of MCE algorithm makes it convenient to conduct feature extraction and classification jointly. Conventional feature extraction and pattern classification algorithms, LDA, PCA, MCE training algorithm, minimum distance classifier, likelihood classifier and Bayesian classifier, are linear algorithms. The advantage of linear algorithms is their simplicity and ability to reduce feature dimensionalities. However, they have the limitation that the decision boundaries generated are linear and have little computational flexibility. SVM is a recently developed integrated pattern classification algorithm with non-linear formulation. It is based on the idea that the classification that a.ords dot-products can be computed efficiently in higher dimensional feature spaces. The classes which are not linearly separable in the original parametric space can be linearly separated in the higher dimensional feature space. Because of this, SVM has the advantage that it can handle the classes with complex nonlinear decision boundaries. However, SVM is a highly integrated and closed pattern classification system. It is very difficult to adopt feature extraction into SVM’s framework. Thus SVM is unable to conduct feature extraction tasks. This thesis investigates LDA and PCA for feature extraction and dimensionality reduction and proposes the application of MCE training algorithms for joint feature extraction and classification tasks. A generalized MCE (GMCE) training algorithm is proposed to mend the shortcomings of the MCE training algorithms in joint feature and classification tasks. SVM, as a non-linear pattern classification system is also investigated in this thesis. A reduced-dimensional SVM (RDSVM) is proposed to enable SVM to conduct feature extraction and classification jointly. All of the investigated and proposed algorithms are tested and compared firstly on a number of small databases, such as Deterding Vowels Database, Fisher’s IRIS database and German’s GLASS database. Then they are tested in a large-scale speech recognition experiment based on TIMIT database.
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Books on the topic "Linear Pattern Recognition"

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1941-, Hart Peter E., and Stork David G, eds. Pattern classification. 2nd ed. New York: Wiley, 2001.

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Hildenbrand, Dietmar. Foundations of Geometric Algebra Computing. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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Gainanov, Damir. Graphs for Pattern Recognition: Infeasible Systems of Linear Inequalities. de Gruyter GmbH, Walter, 2016.

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Gainanov, Damir. Graphs for Pattern Recognition: Infeasible Systems of Linear Inequalities. De Gruyter, Inc., 2016.

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Gainanov, Damir. Graphs for Pattern Recognition: Infeasible Systems of Linear Inequalities. de Gruyter GmbH, Walter, 2016.

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Matrix Methods in Data Mining and Pattern Recognition. Society for Industrial and Applied Mathematics, 2019.

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Geometric Control of Patterned Linear Systems Lecture Notes in Control and Information Sciences. Springer, 2012.

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Matrix Methods in Data Mining and Pattern Recognition (Fundamentals of Algorithms). Society for Industrial and Applied Mathematics, 2007.

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Duda, Richard O., David G. Stork, and Peter E. Hart. Pattern Classification. Wiley & Sons, Incorporated, John, 2009.

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Duda, Richard O. Pattern Classification. Wiley & Sons, Limited, John, 2013.

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Book chapters on the topic "Linear Pattern Recognition"

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Okada, Kazunori, and Christoph von der Malsburg. "Face Recognition and Pose Estimation with Parametric Linear Subspaces." In Applied Pattern Recognition, 49–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-76831-9_3.

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Devroye, Luc, László Györfi, and Gábor Lugosi. "Linear Discrimination." In A Probabilistic Theory of Pattern Recognition, 39–59. New York, NY: Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4612-0711-5_4.

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Scheunders, P., S. De Backer, and A. Naud. "Non-linear mapping for feature extraction." In Advances in Pattern Recognition, 823–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0033307.

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Devroye, Luc, László Györfi, and Gábor Lugosi. "Generalized Linear Discrimination." In A Probabilistic Theory of Pattern Recognition, 279–88. New York, NY: Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4612-0711-5_17.

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Martínez-Díaz, Saúl, and Javier A. Carmona-Troyo. "Fingerprint Verification with Non-linear Composite Correlation Filters." In Advances in Pattern Recognition, 90–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15992-3_10.

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Schleif, Frank-Michael, and Andrej Gisbrecht. "Data Analysis of (Non-)Metric Proximities at Linear Costs." In Similarity-Based Pattern Recognition, 59–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39140-8_4.

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Rueda, Luis, and B. John Oommen. "The Foundational Theory of Optimal Bayesian Pairwise Linear Classifiers." In Advances in Pattern Recognition, 581–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-44522-6_60.

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Shao, Guowan, Fanmao Liu, and Chunjiang Peng. "Semi-supervised Uncertain Linear Discriminant Analysis." In Pattern Recognition and Computer Vision, 149–60. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60636-7_13.

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Haslett, J., and G. Horgan. "Linear Models in Spatial Discriminant Analysis." In Pattern Recognition Theory and Applications, 47–55. Berlin, Heidelberg: Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/978-3-642-83069-3_4.

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Ram, Surinder, Horst Bischof, and Josef Birchbauer. "Detection of Singularities in Fingerprint Images Using Linear Phase Portraits." In Advances in Pattern Recognition, 349–62. London: Springer London, 2009. http://dx.doi.org/10.1007/978-1-84882-385-3_15.

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Conference papers on the topic "Linear Pattern Recognition"

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Stephen, Priya, and Suresh Jaganathan. "Linear regression for pattern recognition." In 2014 International Conference on Green Computing Communication and Electrical Engineering (ICGCCEE). IEEE, 2014. http://dx.doi.org/10.1109/icgccee.2014.6921393.

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Lai, Po-Hsiang, and Joseph A. O'Sullivan. "Pattern Recognition System Design with Linear Encoding for Discrete Patterns." In 2007 IEEE International Symposium on Information Theory. IEEE, 2007. http://dx.doi.org/10.1109/isit.2007.4557243.

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Rahmati, Mohammad, Laurence G. Hassebrook, Hsienchung Chi, Gongliang Guo, and William A. Gruver. "Tactile pattern recognition with complex linear morphology." In Aerospace Sensing, edited by David P. Casasent and Andrew G. Tescher. SPIE, 1992. http://dx.doi.org/10.1117/12.60547.

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Li, Tiejun, Yanli Wang, Zhe Chen, and Renxiang Wang. "Linear feature extraction for infrared image." In Multispectral Image Processing and Pattern Recognition, edited by Tianxu Zhang, Bir Bhanu, and Ning Shu. SPIE, 2001. http://dx.doi.org/10.1117/12.441474.

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Hotta, Kazuhiro. "Non-linear feature extraction by linear PCA using local kernel." In 2008 19th International Conference on Pattern Recognition (ICPR). IEEE, 2008. http://dx.doi.org/10.1109/icpr.2008.4761721.

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Coggins, J. M. "Non-linear feature space transformations." In IEE Colloquium on Applied Statistical Pattern Recognition. IEE, 1999. http://dx.doi.org/10.1049/ic:19990374.

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Joko, Masao, Yoshinobu Kawahara, and Takehisa Yairi. "Learning Non-linear Dynamical Systems by Alignment of Local Linear Models." In 2010 20th International Conference on Pattern Recognition (ICPR). IEEE, 2010. http://dx.doi.org/10.1109/icpr.2010.271.

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Graf, Norman A. "Pattern recognition and track fitting in central trackers." In Physics and experiments with future linear e+ e- colliders. AIP, 2001. http://dx.doi.org/10.1063/1.1394438.

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Riasati, Vahid R., Christopher O'hara, and Patrick Schuetterle. "Stochastic gradient descent implementation of the modified forward-backward linear prediction." In Pattern Recognition and Tracking XXIX, edited by Mohammad S. Alam. SPIE, 2018. http://dx.doi.org/10.1117/12.2305101.

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Loog, M. "Conditional Linear Discriminant Analysis." In 18th International Conference on Pattern Recognition (ICPR'06). IEEE, 2006. http://dx.doi.org/10.1109/icpr.2006.402.

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