Dissertations / Theses on the topic 'Implementation of unary classifier'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 20 dissertations / theses for your research on the topic 'Implementation of unary classifier.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse dissertations / theses on a wide variety of disciplines and organise your bibliography correctly.
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.
Full textSpenner, 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.
Full textTitle from PDF title page (viewed Mar. 16, 2009). Source: Masters Abstracts International, Volume: 46-05, page: 2734. Adviser: Mitchell A. Thornton. Includes bibliographical references.
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.
Full textHertz, 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.
Full textSarda, 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.
Full textSantos, 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.
Full textSantos, 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.
Full textLindholm, 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.
Full textCutno, Patrick. "Automatic Modulation Classifier - A Blind Feature-Based Tool." Miami University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=miami1480079193743277.
Full textFares, 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.
Full textGeorge, Suma. "Simulink modeling and implementation of cmos dendrites using fpaa." Thesis, Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/44915.
Full textHsu, Ching-Hung, and 許景竤. "General Classifier System implementation as Fuzzy Classifier System." Thesis, 1996. http://ndltd.ncl.edu.tw/handle/78419566846787820224.
Full text國立交通大學
資訊科學學系
84
Knowledge acquisition means to learn knowledge from human or environment which can be reexamined and verified by human. In symbolic AI, to design a learning mechanism on human-like knowledge representation, which is usually complex, is difficult. On the other hand, to convert internal machine knowledge representation used by numerical AI systems into understandable form is also a challenging task. In the AI field, hybrid models of different systems have become a more and more important topic. A hybrid model which combines and coordinates many different techniques can show individual power behind each component techniques and compensate their drawbacks. This thesis combines four different techniques in two major branches of AI, symbolic AI and numerical AI, they are: neural networks, genetic algorithms, fuzzy logic, and Holland''s classifier system. We use this hybrid model to solve the knowledge acquisition problem of a rule-based system. We implement it in a fuzzy logic system. This thesis also improves an existing neuro-fuzzy hybrid model. We compare the knowledge learned by the two models in an iris clustering problem.
Xu, Ying-Hong, and 許景竤. "General Classifier System implementation as Fuzzy Classifier System." Thesis, 1996. http://ndltd.ncl.edu.tw/handle/46714621442698993019.
Full textming-rui-chung and 鍾明瑞. "The Design and Implementation of a Temporal Packet Classifier." Thesis, 2000. http://ndltd.ncl.edu.tw/handle/91186024072638404685.
Full text中原大學
資訊工程學系
89
A Packet Classifier is a program that inspects the values of selected header fields of incoming network packets to determine how each packet is processed. Packet Classifier is part of many network services such as firewalls, quality of services (QoS) and network monitoring. Recently, there is a new network attack, called Denial of Service (DOS) attack, which transmits a large number of packets to block a network service. This kind of network attack can be detected by temporal relationship of successive packets. In this thesis, we will introduce a Temporal Packet Classifier, which supports multi-fields matching and logical expression between fields. It also provides an iterator operator to match temporally related packets. This is useful in network monitoring and attack detection. Our Temporal Packet Classifier classifies packets according to rules. Each rule contains matching fields and logical relations between these fields. In matching rule, the packet fields are matched first, then the logical expression of each rule is executed. We implement this packet classifier in Linux kernel and give some performance data.
YANG, SHAO-WEI, and 楊邵為. "The Design and Implementation of Emotion Classifier for Facebook Articles." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/72g2md.
Full text國立中正大學
資訊工程研究所
105
In recent years, Public Opinion Analysis, Market Research and Chatbot have been booming day by day. Sentiment Analysis is a basis for these applications and the result of the Sentiment Analysis is related to the quality of the lexicons and the corpus being used. In this thesis, we built a Chinese Sentiment Lexicon. We referred to the popular Lexicons such as DUTIR and proposed a modified classification structure. We crawled millions of Facebook articles as corpus to compute the term frequency, and selected terms with non-trivial frequency that are related to Sentiment into our Lexicon. A web application is developed to perform sentiment analysis on articles. The program will provide a positive-vs-negative assessment plus sentiment classification for the articles. The experiments showed that our Lexicon is more precise than the DUTIR on the Facebook articles.
Wu, Wen-Feng, and 吳文峰. "Design and Implementation of A Classifier for Chinese E-mails." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/5uy5d3.
Full text逢甲大學
資訊工程所
90
E-mail has become an indispensable part in many people''s life because of the popularity of the Internet. The amounts and types of mails increase day after day. In order to facilitate the searching when the user is looking for a previously received mail, many users organize their mails to the different folders. Traditionally, the users filter their mails manually. Some e-mail applications allow the users to classify their mails by setting filtering rules. However, it may need experiences to set those complex rules. It''s also not easy to analyze and to produce a set of rules that can represent the whole class precisely. In order to facilitate the filtering, many researches that apply techniques of machine learning to classify documents automatically in recent years. The purpose of this paper is to design a classifier system that is suitable for Chinese mails. We use different classifier adapted to different feature sets, and combine classifiers to classify the mails. We embed our email classifier in a mail server to tag mails with class labels. The users can category their mails directly according to this class label. This will reduce the burden of the users to classify the mails and facilitate the difficulty of set those complex rules. Experiments on our personal email dataset shows that we proposed system applying in the email classification has its acceptable accuracy.
陳俊銘. "The design and implementation of CAM-based IPv6 packet classifier." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/24701139874540081833.
Full text"Design and implementation of multistage tree classifier for Chinese character recognition." Chinese University of Hong Kong, 1992. http://library.cuhk.edu.hk/record=b5887069.
Full textThesis (M.Sc.)--Chinese University of Hong Kong, 1992.
Includes bibliographical references (leaves [14-15]).
PREFACE
ABSTRACT
CONTENT
Chapter §1. --- INTRODUCTION
Chapter §1.1 --- The Chinese language --- p.1
Chapter §1.2 --- Chinese information processing system --- p.2
Chapter §1.3 --- Chinese character recognition --- p.4
Chapter §1.4 --- Multi-stage tree classifier Vs Single-stage tree classifier in Chinese character recognition --- p.6
Chapter §1.5 --- Decision Tree
Chapter §1.5.1 --- Basic Terminology of a decision tree --- p.7
Chapter §1.5.2 --- Structure design of a decision tree --- p.10
Chapter §1.6 --- Motivation of the project --- p.12
Chapter §1.7 --- Objects of the project --- p.14
Chapter §1.8 --- Development environment --- p.14
Chapter §2. --- APPROACH 1 - UNSUPERVISED LEARNING --- p.15
Chapter §3. --- APPROACH 2 - SUPERVISED LEARNING
Chapter §3.1 --- Idea --- p.17
Chapter §3.2 --- The 3 Corner Code --- p.20
Chapter §3.3 --- Feature Extraction & Selection --- p.22
Chapter §3.4 --- Decision at Each Node
Chapter §3.4.1 --- Statistical Linear Discriminant Analysis --- p.22
Chapter §3.4.2 --- Optimization of the Number of Misclassification --- p.24
Chapter §3.5 --- Implementation
Chapter §3.5.1 --- Training Data --- p.36
Chapter §3.5.2 --- Clustering with the Use of SAS --- p.38
Chapter §3.5.3 --- Building the Decision Trees --- p.42
Chapter §3.5.4 --- Description of the Classifier --- p.45
Chapter §3.6 --- Experiments and Testing Result
Chapter §3.6.1 --- Performance Parameters being Measured --- p.47
Chapter §3.6.2 --- Testing by Resubstitution Method --- p.50
Chapter §3.6.3 --- Noise Model --- p.52
Chapter §4. --- POSSIBLE IMPROVEMENT --- p.55
Chapter §5. --- EXPERIMENTAL RESULTS & THE IMPROVED MULTISTAGE CLASSIFIER
Chapter §5.1 --- Experimental Results --- p.59
Chapter §5.2 --- Conclusion --- p.70
Chapter §6. --- IMPROVED MULTISTAGE TREE CLASSIFIER
Chapter §6.1 --- The Optimal Multistage Tree Classifier --- p.72
Chapter §6.2 --- Performance Analysis --- p.73
Chapter §7. --- FURTHER DISCRIMINATION BY CONTEXT CONSIDERATION
Chapter §7.1 --- Idea --- p.76
Chapter §7.2 --- Description of Algorithm --- p.78
Chapter §7.3 --- Performance Analysis --- p.81
Chapter §8. --- CONCLUSION
Chapter §8.1 --- Advantage of the Classifier --- p.84
Chapter §8.2 --- Limitation of the Classifier --- p.85
Chapter §9. --- AREA OF FUTURE RESEARCH AND IMPROVEMENT
Chapter §9.1 --- Detailed Analysis at Each Terminal Node --- p.86
Chapter §9.2 --- Improving the Noise Filtering Technique --- p.87
Chapter §9.3 --- The Use of 4 Corner Code --- p.88
Chapter §9.4 --- Increase in the Dimension of the Feature Space --- p.90
Chapter §9.5 --- 1-Tree Protocol with Entropy Reduction --- p.91
Chapter §9.6 --- The Use of Human Intelligence --- p.92
APPENDICES
Chapter A.1 --- K-MEANS
Chapter A.2 --- Unsupervised Learning Approach
Chapter A.3 --- Other Algorithms (Maximum Distance & ISODATA)
Chapter A.4 --- Possible Improvement
Chapter A.5 --- Theories on Statistical Discriminant Analysis
Chapter A.6 --- Passage used in Testing the Performance of the Classifier with Context Consideration
Chapter A.7 --- A Partial List of Semantically Related Chinese Characters
Chapter A.8 --- An Example of Misclassification Table
Chapter A.9 --- "Listing of the Program ""CHDIS.C"""
REFERENCE
Chen, Chien-Hua, and 陳建華. "The Design and Implementation of Protocol Classifier based on Linux Netfilter." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/83719500733493832247.
Full text國立中山大學
資訊工程學系研究所
94
The management of network bandwidth is more important along with the population growth of Internet. For the issue of network bandwidth management the first thing needs to be done is to analyze network traffic belongs to which protocol. And then we can restrict the usage of network bandwidth accroding to the mangement policy. The mean used to identify network traffic in the past is port-based one which based on the well-known default port number of application protocols. For example, the Hyper-Text Transfer Protocol (HTTP) uses port number 80 as his default port, therefor we could classify traffic which appears in port 80 as HTTP traffic. It is not enough for applications in our own day, especilly the Peer-to-Peer application that used random port number as his default port in order to evade the port-based classifiaction. In order to conquer the issue described above we developed a content-based protocol classifier which inspects the payload of packets. We also compared our system with other content-based protocol classifiers. In addition, we also provided a verification tool which verifies the result of protocol classifier by connecting to the host and testing the hehavior of specific application.
Yang, Tang-chun, and 楊棠鈞. "Implementation of a Road Sign Recognition System Based on Integration of Adaboost Classifier and Support Vector Machine." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/28850598979480975307.
Full text國立成功大學
工程科學系碩博士班
97
Many different automatic technologies have been used to develop driver assistance systems for improving the safety of driving. Road sign recognition system is an important subsystem of a driver assistance system. It can be used to provide the driver about the road sign information in front of the vehicle. In this thesis, a road sign recognition system was proposed which combined Adaboost classifier and support vector machine(SVM) to do the road sign detection and the content recognition, respectively. In the content representation phase, the Canny edge detection method was adopted to obtain the feature vector, to be recognized by SVM, of the detected road sign image. The proposed system can detect the road signs correctly for the captured image under the conditions such as low illumination, rotation, occlusion and rich red color. From the experimental results, it is shown that the proposed system can perform well, under accepted recognition rate, for video input. It is encouraged to apply the proposed system to a real time application.