Relevant bibliographies by topics / Implementation of unary classifier

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Implementation of unary classifier'

Author: Grafiati
Published: 6 September 2021
Last updated: 18 February 2022

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Journal articles on the topic "Implementation of unary classifier"

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Rangkuti, Rizki Perdana, Vektor Dewanto, Aprinaldi, and Wisnu Jatmiko. "Utilizing Google Images for Training Classifiers in CRF-Based Semantic Segmentation." Journal of Advanced Computational Intelligence and Intelligent Informatics 20, no. 3 (May 19, 2016): 455–61. http://dx.doi.org/10.20965/jaciii.2016.p0455.

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One promising approach to pixel-wise semantic segmentation is based on conditional random fields (CRFs). CRF-based semantic segmentation requires ground-truth annotations to supervisedly train the classifier that generates unary potentials. However, the number of (public) annotation data for training is limitedly small. We observe that the Internet can provide relevant images for any given keywords. Our idea is to convert keyword-related images to pixel-wise annotated images, then use them as training data. In particular, we rely on saliency filters to identify the salient object (foreground) of a retrieved image, which mostly agrees with the given keyword. We utilize saliency information for back-and-foreground CRF-based semantic segmentation to further obtain pixel-wise ground-truth annotations. Experiment results show that training data from Google images improves both the learning performance and the accuracy of semantic segmentation. This suggests that our proposed method is promising for harvesting substantial training data from the Internet for training the classifier in CRF-based semantic segmentation.
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Efremides, O. B., M. P. Bekakos, and D. J. Evans. "Integrated classifier simulator and neurochip VHDL implementation." International Journal of Computer Mathematics 80, no. 11 (November 2003): 1343–50. http://dx.doi.org/10.1080/0020716031000148223.

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Tran, Hoai Linh, Van Nam Pham, and Duc Thao Nguyen. "A hardware implementation of intelligent ECG classifier." COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering 34, no. 3 (May 5, 2015): 905–19. http://dx.doi.org/10.1108/compel-05-2014-0119.

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Purpose – The purpose of this paper is to design an intelligent ECG classifier using programmable IC technologies to implement many functional blocks of signal acquisition and processing in one compact device. The main microprocessor also simulates the TSK neuro-fuzzy classifier in testing mode to recognize the ECG beats. The design brings various theoretical solutions into practical applications. Design/methodology/approach – The ECG signals are acquired and pre-processed using the Field-Programmable Analog Array (FPAA) IC due to the ability of precise configuration of analog parameters. The R peak of the QRS complexes and a window of 300 ms of ECG signals around the R peak are detected. In this paper we have proposed a method to extract the signal features using the Hermite decomposition algorithm, which requires only a multiplication of two matrices. Based on the features vectors, the ECG beats are classified using a TSK neuro-fuzzy network, whose parameters are trained earlier on PC and downloaded into the device. The device performance was tested with the ECG signals from the MIT-BIH database to prove the correctness of the hardware implementations. Findings – The FPAA and Programmable System on Chip (PSoC) technologies allow us to integrate many signal processing blocks in a compact device. In this paper the device has the same performance in ECG signal processing and classifying as achieved on PC simulators. This confirms the correctness of the implementation. Research limitations/implications – The device was fully tested with the signals from the MIT-BIH databases. For new patients, we have tested the device in collecting the ECG signals and QRS detections. We have not created a new database of ECG signals, in which the beats are examined by doctors and annotated the type of the rhythm (normal or abnormal, which type of arrhythmia, etc.) so we have not tested the classification mode of the device on real ECG signals. Social implications – The compact design of an intelligent ECG classifier offers a portable solution for patients with heart diseases, which can help them to detect the arrhythmia on time when the doctors are not nearby. This type of device not only may help to improve the patients’ safety but also contribute to the smart, inter-networked life style. Originality/value – The device integrate a number of solutions including software, hardware and algorithms into a single, compact device. Thank to the advance of programmable ICs such as FPAA and PSoC, the designed device can acquire one channel of ECG signals, extract the features and classify the arrhythmia type (if detected) using the neuro-fuzzy TSK network in online mode.
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Ren, Jian Si. "Research and Implementation of Text Classification Algorithm." Applied Mechanics and Materials 644-650 (September 2014): 2395–98. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.2395.

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The development of Internet and digital library has triggered a lot of text categorization methods. How to find desired information accurately and timely is becoming more and more important and automatic text categorization can help us achieve this goal. In general, text classifier is implemented by using some traditional classification methods such as Naive-Bayes (NB). ARC-BC (Associative Rule-based Classifier by Category) can be used for text categorization by dividing text documents into subsets in which all documents belong to the same category and generate associative classification rules for each subset. This classifier differs from previous methods in that it consists of discovered association rules between words and categories extracted from the training set. In order to train and test this classifier, we constructed training data and testing data respectively by selecting documents from Yahoo. The experimental result shows that the performance of ARC-BC based text categorization is very pretty efficient and effective and it is comparable to Naïve Bayesian algorithm based text categorization.
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MONSERRAT, M., F. ROSSELLÓ, and J. TORRENS. "WHEN IS A CATEGORY OF MANY-SORTED PARTIAL ALGEBRAS CARTESIAN-CLOSED?" International Journal of Foundations of Computer Science 06, no. 01 (March 1995): 51–66. http://dx.doi.org/10.1142/s0129054195000056.

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In this paper we study the cartesian closedness of the five most natural categories with objects all partial many-sorted algebras of a given signature. In particular, we prove that, from these categories, only the usual one and the one having as morphisms the closed homomorphisms can be cartesian closed. In the first case, it is cartesian closed exactly when the signature contains no operation symbol, in which case such a category is a slice category of sets. In the second case, it is cartesian closed if and only if all operations are unary. In this case, we identify it as a functor category and we show some relevant constructions in it, such as its subobjects classifier or the exponentials.
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Lee, Hansoo, and Sungshin Kim. "Black-Box Classifier Interpretation Using Decision Tree and Fuzzy Logic-Based Classifier Implementation." International Journal of Fuzzy Logic and Intelligent Systems 16, no. 1 (March 31, 2016): 27–35. http://dx.doi.org/10.5391/ijfis.2016.16.1.27.

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Chang, Yong Hoon, Kwon Soon Lee, and Gye Rok Jun. "The Implementation of Pattern Classifier for Karyotype Classification." Journal of Korean Society of Medical Informatics 3, no. 2 (1997): 207. http://dx.doi.org/10.4258/jksmi.1997.3.2.207.

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Sulistiyo, Mahmud Dwi, and Rita Rismala. "Implementation of Evolution Strategies for Classifier Model Optimization." Indonesian Journal on Computing (Indo-JC) 1, no. 2 (December 30, 2016): 13. http://dx.doi.org/10.21108/indojc.2016.1.2.43.

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<span style="font-size: 9.0pt; mso-bidi-font-size: 11.0pt; line-height: 107%; font-family: 'Times New Roman','serif'; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;">Classification becomes one of the classic problems that are often encountered in the field of artificial intelligence and data mining. The problem in classification is how to build a classifier model through training or learning process. Process in building the classifier model can be seen as an optimization problem. Therefore, optimization algorithms can be used as an alternative way to generate the classifier models. In this study, the process of learning is done by utilizing one of Evolutionary Algorithms (EAs), namely Evolution Strategies (ES). Observation and analysis conducted on several parameters that influence the ES, as well as how far the general classifier model used in this study solve the problem. The experiments and analyze results show that ES is pretty good in optimizing the linear classification model used. For Fisher’s Iris dataset, as the easiest to be classified, the test accuracy is best achieved by 94.4%; KK Selection dataset is 84%; and for SMK Major Election datasets which is the hardest to be classified reach only 49.2%.</span>
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Jajoo, Gaurav, Yogesh Kumar, Ashok Kumar, and Sandeep Kumar Yadav. "Implementation of modulation classifier over software defined radio." IET Communications 14, no. 9 (June 2, 2020): 1467–75. http://dx.doi.org/10.1049/iet-com.2019.0922.

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Zhang, Bin, Cunpeng Wang, Yonglin Shen, and Yueyan Liu. "Fully Connected Conditional Random Fields for High-Resolution Remote Sensing Land Use/Land Cover Classification with Convolutional Neural Networks." Remote Sensing 10, no. 12 (November 27, 2018): 1889. http://dx.doi.org/10.3390/rs10121889.

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The interpretation of land use and land cover (LULC) is an important issue in the fields of high-resolution remote sensing (RS) image processing and land resource management. Fully training a new or existing convolutional neural network (CNN) architecture for LULC classification requires a large amount of remote sensing images. Thus, fine-tuning a pre-trained CNN for LULC detection is required. To improve the classification accuracy for high resolution remote sensing images, it is necessary to use another feature descriptor and to adopt a classifier for post-processing. A fully connected conditional random fields (FC-CRF), to use the fine-tuned CNN layers, spectral features, and fully connected pairwise potentials, is proposed for image classification of high-resolution remote sensing images. First, an existing CNN model is adopted, and the parameters of CNN are fine-tuned by training datasets. Then, the probabilities of image pixels belong to each class type are calculated. Second, we consider the spectral features and digital surface model (DSM) and combined with a support vector machine (SVM) classifier, the probabilities belong to each LULC class type are determined. Combined with the probabilities achieved by the fine-tuned CNN, new feature descriptors are built. Finally, FC-CRF are introduced to produce the classification results, whereas the unary potentials are achieved by the new feature descriptors and SVM classifier, and the pairwise potentials are achieved by the three-band RS imagery and DSM. Experimental results show that the proposed classification scheme achieves good performance when the total accuracy is about 85%.
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Dissertations / Theses on the topic "Implementation of unary classifier"

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Beneš, Jiří. "Unární klasifikátor obrazových dat." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-442432.

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The work deals with an introduction to classification algorithms. It then divides classifiers into unary, binary and multi-class and describes the different types of classifiers. The work compares individual classifiers and their areas of use. For unary classifiers, practical examples and a list of used architectures are given in the work. The work contains a chapter focused on the comparison of the effects of hyper parameters on the quality of unary classification for individual architectures. Part of the submission is a practical example of reimplementation of the unary classifier.
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Spenner, Laura. "Quantum logic implementation of unary arithmetic operations with inheritance." Ann Arbor, Mich. : ProQuest, 2008. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:1452767.

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Thesis (M.S. in Computer Engineering)--S.M.U.
Title from PDF title page (viewed Mar. 16, 2009). Source: Masters Abstracts International, Volume: 46-05, page: 2734. Adviser: Mitchell A. Thornton. Includes bibliographical references.
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Hertz, Erik, and Peter Nilsson. "A Methodology for Parabolic Synthesis of Unary Function for Hardware Implementation." Department of Electrical and Information Technology, Lund University, Lund, Sweden, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-22325.

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Hertz, Erik. "Methodologies for Approximation of Unary Functions and Their Implementation in Hardware." Doctoral thesis, Högskolan i Halmstad, Centrum för forskning om inbyggda system (CERES), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-30983.

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Applications in computer graphics, digital signal processing, communication systems, robotics, astrophysics, fluid physics and many other areas have evolved to become very computation intensive. Algorithms are becoming increasingly complex and require higher accuracy in the computations. In addition, software solutions for these applications are in many cases not sufficient in terms of performance. A hardware implementation is therefore needed. A recurring bottleneck in the algorithms is the performance of the approximations of unary functions, such as trigonometric functions, logarithms and the square root, as well as binary functions such as division. The challenge is therefore to develop a methodology for the implementation of approximations of unary functions in hardware that can cope with the growing requirements. The methodology is required to result in fast execution time, low complexity basic operations that are simple to implement in hardware, and – sincemany applications are battery powered – low power consumption. To ensure appropriate performance of the entire computation in which the approximation is a part, the characteristics and distribution of the approximation error are also things that must be possible to manage. The new approximation methodologies presented in this thesis are of the type that aims to reduce the sizes of the look-up tables by the use of auxiliary functions. They are founded on a synthesis of parabolic functions by multiplication – instead of addition, which is the most common. Three approximation methodologies have been developed; the two last being further developments of the first. For some functions, such as roots, inverse and inverse roots, a straightforward solution with an approximation is not manageable. Since these functions are frequent in many computation intensive algorithms, it is necessary to find very efficient implementations of these functions. New methods for this are also presented in this thesis. They are all founded on working in a floating-point format, and, for the roots functions, a change of number base is also used. The transformations not only enable simpler solutions but also increased accuracy, since the approximation algorithm is performed on a mantissa of limited range. Tools for error analysis have been developed as well. The characteristics and distribution of the approximation error in the new methodologies are presented and compared with existing state-of-the-art methods such as CORDIC. The verification and evaluation of the solutions have to a large extent been made as comparative ASIC implementations with other approximation methods, separately or embedded in algorithms. As an example, an implementation of the logarithm made using the third methodology developed, Harmonized Parabolic Synthesis (HPS), is compared with an implementation using the CORDIC algorithm. Both implementations are designed to provide 15-bit resolution. The design implemented using HPS performs 12 times better than the CORDIC implementation in terms of throughput. In terms of energy consumption, the new methodology consumes 96% less. The chip area is 60% smaller than for the CORDIC algorithm. In summary, the new approximation methodologies presented are found to well meet the demanding requirements that exist in this area.
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Sarda, Deepak Prasad. "Implementation and evaluation of an accurate real-time voiceband signal classifier." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape8/PQDD_0015/MQ47157.pdf.

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Santos, Luís André Brísio Marques dos. "Implementation and evaluation of a spam classifier based on the dynamic behaviour of immune cells." Dissertação, Porto : [s. n.], 2008. http://catalogo.up.pt/F?func=find-b&local_base=FCB01&find_code=SYS&request=000101268.

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Santos, Luís André Brísio Marques dos. "Implementation and evaluation of a spam classifier based on the dynamic behaviour of immune cells." Master's thesis, Porto : [s. n.], 2008. http://hdl.handle.net/10216/64160.

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Lindholm, Alexander. "A study about fraud detection and the implementation of SUSPECT - Supervised and UnSuPervised Erlang Classifier Tool." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-222774.

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Fraud detection is a game of cat and mouse between companies and people trying to commit fraud. Most of the work within the area is not published due to several reasons. One of the reasons is that if a company publishes how their system works, the public will know how to evade detection. This paper describes the implementation of a proof-of-concept fraud detection system. The prototype  named SUSPECT uses two different methods for fraud detection. The first one being a supervised classifier in form of an artificial neural network and the second one being an unsupervised classifier in the form of clustering with outlier detection. The two systems are compared with each other as well as with other systems within the field. The paper ends with conclusions and suggestions on how to to make SUSPECT perform better.
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Cutno, Patrick. "Automatic Modulation Classifier - A Blind Feature-Based Tool." Miami University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=miami1480079193743277.

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Fares, George E. "Probabilistic fault location in combinational logic networks by multistage binary tree classifier algorith development, implementation results and efficiency." Thesis, University of Ottawa (Canada), 1989. http://hdl.handle.net/10393/5937.

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Books on the topic "Implementation of unary classifier"

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Implementation and Analysis of the Parallel Genetic Rule and Classifier Construction Environment. Storming Media, 2001.

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Book chapters on the topic "Implementation of unary classifier"

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Glöckler, Jens. "Forgetting Automata and Unary Languages." In Implementation and Application of Automata, 186–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11812128_18.

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Pighizzini, Giovanni. "Unary Language Concatenation and Its State Complexity." In Implementation and Application of Automata, 252–62. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44674-5_21.

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De Biasi, Marzio, and Abuzer Yakaryılmaz. "Unary Languages Recognized by Two-Way One-Counter Automata." In Implementation and Application of Automata, 148–61. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08846-4_11.

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Pighizzini, Giovanni. "Investigations on Automata and Languages over a Unary Alphabet." In Implementation and Application of Automata, 42–57. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08846-4_3.

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Sosa-Sosa, Victor J., Ivan Lopez-Arevalo, Omar Jasso-Luna, and Hector Fraire-Huacuja. "Distributed Implementation of an Intelligent Data Classifier." In Soft Computing for Recognition Based on Biometrics, 73–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15111-8_5.

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Evsukoff, Alexandre G., Myrian C. A. Costa, and Nelson F. F. Ebecken. "Parallel Implementation of a Fuzzy Rule Based Classifier." In High Performance Computing for Computational Science - VECPAR 2004, 184–93. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11403937_15.

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Holzer, Markus, and Christian Rauch. "The Range of State Complexities of Languages Resulting from the Cascade Product—The Unary Case (Extended Abstract)." In Implementation and Application of Automata, 90–101. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-79121-6_8.

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Siniscalchi, Sabato M., Fulvio Gennaro, Salvatore Vitabile, Antonio Gentile, and Filippo Sorbello. "Efficient FPGA Implementation of a Knowledge-Based Automatic Speech Classifier." In Embedded Software and Systems, 198–209. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11599555_21.

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Zhou, S. Kevin. "A Binary Decision Tree Implementation of a Boosted Strong Classifier." In Lecture Notes in Computer Science, 198–212. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11564386_16.

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Ikbal, Mohammad Rafi, Mahmoud Fayez, Mohammed M. Fouad, and Iyad Katib. "Fast Implementation of Face Detection Using LPB Classifier on GPGPUs." In Advances in Intelligent Systems and Computing, 1036–47. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22871-2_74.

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Conference papers on the topic "Implementation of unary classifier"

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Thornton, Mitchell A., David W. Matula, Laura Spenner, and D. Michael Miller. "Quantum Logic Implementation of Unary Arithmetic Operations." In 2008 38th International Symposium on Multiple Valued Logic (ismvl 2008). IEEE, 2008. http://dx.doi.org/10.1109/ismvl.2008.27.

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Hertz, Erik, and Peter Nilsson. "A methodology for parabolic synthesis of unary functions for hardware implementation." In 2008 2nd International Conference on Signals, Circuits and Systems (SCS). IEEE, 2008. http://dx.doi.org/10.1109/icscs.2008.4746866.

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Ju, Xu Chan, and Ying Jie Tian. "Efficient Implementation of Nonparallel Hyperplanes Classifier." In 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT). IEEE, 2012. http://dx.doi.org/10.1109/wi-iat.2012.30.

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Yang, Feng-Jen. "An Implementation of Naive Bayes Classifier." In 2018 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2018. http://dx.doi.org/10.1109/csci46756.2018.00065.

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Ellawala, Nadun, and Subramaniam Thayaparan. "Hardware Implementation of EEG Classifier Using LDA." In 2019 2nd International Conference on Bioinformatics, Biotechnology and Biomedical Engineering (BioMIC) - Bioinformatics and Biomedical Engineering. IEEE, 2019. http://dx.doi.org/10.1109/biomic48413.2019.9034742.

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Wasef, Michael R., and Nader Rafla. "HLS Implementation of Linear Discriminant Analysis Classifier." In 2020 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE, 2020. http://dx.doi.org/10.1109/iscas45731.2020.9181270.

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Yau, H. C., and M. T. Manry. "Sigma-pi implementation of a Gaussian classifier." In 1990 IJCNN International Joint Conference on Neural Networks. IEEE, 1990. http://dx.doi.org/10.1109/ijcnn.1990.137938.

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Ulfa, Dinah K., and Dwi H. Widyantoro. "Implementation of haar cascade classifier for motorcycle detection." In 2017 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom). IEEE, 2017. http://dx.doi.org/10.1109/cyberneticscom.2017.8311712.

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Bai, Rujiang, and Xiaoyue Wang. "Combination Methodologies of Text Classifier: Design and Implementation." In Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007). IEEE, 2007. http://dx.doi.org/10.1109/fskd.2007.222.

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Yau, H. C., and M. T. Manry. "Sigma-pi implementation of a nearest neighbor classifier." In 1990 IJCNN International Joint Conference on Neural Networks. IEEE, 1990. http://dx.doi.org/10.1109/ijcnn.1990.137645.

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