tailieunhanh - Chapter 1: Overview on Pattern Recognition and Machine Learning

Chapter 1: Overview on Pattern Recognition and Machine Learning includes about Pattern Recognition, Machine learning, Related fields of pattern recognition, Classification, Two paradigms of pattern recognition. | Chapter 1 Overview on Pattern Recognition and Machine Learning Assoc. Prof. Dr. Duong Tuan Anh Faculty of Computer Science and Engineering, HCMC Univ. of Technology 3/2015 Outline Pattern Recognition Machine learning Related fields of pattern recognition Classification Two paradigms of pattern recognition 1. Pattern recognition Humans are good at recognizing objects (or patterns). We find it difficult to write a computer program to recognize objects. Ex: By analyzing sample images of faces, a program should be able to capture the pattern specific to a face and identify it as a face. This is pattern recognition. There may be several classes and we habe to classify a particular face into a certain category (or class). This is classification. In pattern recognition, the term pattern is used to include all objects that we want to classify. A class is a collection of objects that are similar, but not necessarily identical, and which is distinguishable from other classes. Figure . | Chapter 1 Overview on Pattern Recognition and Machine Learning Assoc. Prof. Dr. Duong Tuan Anh Faculty of Computer Science and Engineering, HCMC Univ. of Technology 3/2015 Outline Pattern Recognition Machine learning Related fields of pattern recognition Classification Two paradigms of pattern recognition 1. Pattern recognition Humans are good at recognizing objects (or patterns). We find it difficult to write a computer program to recognize objects. Ex: By analyzing sample images of faces, a program should be able to capture the pattern specific to a face and identify it as a face. This is pattern recognition. There may be several classes and we habe to classify a particular face into a certain category (or class). This is classification. In pattern recognition, the term pattern is used to include all objects that we want to classify. A class is a collection of objects that are similar, but not necessarily identical, and which is distinguishable from other classes. Figure illustrates the difference between classification where the classes are known beforehand and classification where classes are creates after inspecting the objects. Figure Classification when the classes are (a) known and b) unknown beforehand. Applications of pattern recognitions Interest in pattern recognition has grown due to emerging applications. These include: Data mining Biometrics personal identification based on physical attributes of the face, iris, fingerprints, etc. Machine vision automatic visual inspection in an assembly line Character recognition automatic mail sorting by zip code, automatic check scanners at at ATMs. Document recognition recognize whether an email is spam or not, based on the message header and content. Speech recognition helping handicapped patients to control machines. Computer-added diagnosis helping doctors make diagnostic decisions based on interpreting medical data such as ultrasound images, electrocardiograms (ECGs) or .

• Cơ sở dữ liệu
• Pattern Recognition
• Machine Learning
• Related fields of pattern recognition
• Two paradigms of pattern recognition
• Applications of pattern recognitions
• Ebook Pattern Recognition and Machine Learning
• Mathematical notation
• Polynomial curve fitting
• Probability distributions
• Linear basis function models
• Graphical models
• K means clustering
• Bayesian linear regression
• Principal component analysis
• Traffic sign recognition
• Problem of traffic sign recognition
• HOG Feature
• SVM in pattern recognition
• Reading pixel into 1D data
• Activity recognition
• Multi resident homes
• Ontology–based approaches
• Ontology based activity recognition
• Pattern discovering
• Curvelet transform
• Zernike moments
• Analytics set Fluorescence image
• Use Original Zernike moment
• Correlation pattern recognition method
• EJ301 detector
• EJ 301 liquid scintillation
• Pulse shape discriminant
• DCPR method outperformed
• Biological imaging
• Pattern recognition algorithms
• Annotating gene expressions
• Classifying phenotypes
• Different biological problems
• Pattern recognition receptors
• Pathogen associated molecular patterns
• Damageassociated molecula
• Determine assorted group of pathogens
• Core structures called
• BMC Bioinformatics
• Template matching
• Object recognition
• Object localization
• Human computer interaction
• Dynamic hand gesture recognition
• Spatial and temporal Features
• Home appliances
• Cyclical pattern
• Tự động hóa
• Cơ khí chế tạo máy
• Điện – điện tử
• Kiến trúc xây dựng
• kỹ thuật viễn thông
• báo cáo hóa học
• công trình nghiên cứu về hóa học
• tài liệu về hóa học
• cách trình bày báo cáo
• báo cáo khoa học
• Basic Concepts
• Measurement
• Representation
• Paradigms
• Metric Methods
• Neural Net Methods
• Statistical Methods
• Introduction to TCP IP Networks
• Other Types of Hardware
• Computer tips
• information technology
• computer applications
• Báo cáo sinh học hay
• báo cáo sinh học
• công trình nghiên cứu sinh học
• tài liệu về sinh học
• báo cáo y học
• công trình nghiên cứu về y học
• tài liệu về y học
• Report medicine
• traditional medicine
• scientific reports
• molecular Biology
• tumor cells
• Antimicrobial