Syntactic pattern recognition or structural pattern recognition is a form of pattern recognition, in which each object can be represented by a variable-cardinality set of symbolic, nominal features. This allows for representing pattern structures, taking into account more complex interrelationships between attributes than is possible in the case of flat, numerical feature vectors of fixed dimensionality, that are used in statistical classification. Syntactic pattern recognition can be used instead of statistical pattern recognition if there is clear structure in the patterns. One way to present such structure is by means of a strings of symbols from a formal language. In this case the differences in the structures of the classes are encoded as different grammars. An example of this would be diagnosis of the heart with ECG measurements. ECG waveforms can be approximated with diagonal and vertical line segments. If normal and unhealthy waveforms can be described as formal grammars, measured ECG signal can be classified as healthy or unhealthy by first describing it in term of the basic line segments and then trying to parse the descriptions according to the grammars. Another example is tessellation of tiling patterns. A second way to represent relations are graphs, where nodes are connected if corresponding subpatterns are related. An item can be labeled as belonging to a class if its graph representation is isomorphic with prototype graphs of the class. Typically, patterns are constructed from simpler sub patterns in a hierarchical fashion. This helps in dividing the recognition task into easier subtask of first identifying sub patterns and only then the actual patterns. Structural methods provide descriptions of items, which may be useful in their own right. For example, syntactic pattern recognition can be used to find out what objects are present in an image. Furthermore, structural methods are strong in finding a correspondence mapping between two images of an object. Under natural conditions, corresponding features will be in different positions and/or may be occluded in the two images, due to camera-attitude and perspective, as in face recognition. A graph matching algorithm will yield the optimal correspondence. (Wikipedia).
Pattern Matching - Correctness
Learn how to use pattern matching to assist you in your determination of correctness. This video contains two examples, one with feedback and one without. https://teacher.desmos.com/activitybuilder/custom/6066725595e2513dc3958333
From playlist Pattern Matching with Computation Layer
SYN_020 - Linguistic Micro-Lectures: Syntactic Trees
In this short micro-lecture, Aaron Cook, one of Prof. Handke's students, discusses the notion of the "syntactic tree", a central concept in syntax.
From playlist Micro-Lectures - Syntax
Synesthesia - Music Composition
My website: http://oliverlugg.com/ This piece began with the funky piano riff you can hear in the chorus, and grew from there. For those of you who may not know, Synesthesia is the condition of linking ideas within two methods of sensing, such as specific colours being attributed to music
From playlist Music Compositions
Image Recognition and Python Part 1
Sample code for this series: http://pythonprogramming.net/image-recognition-python/ There are many applications for image recognition. One of the largest that people are most familiar with would be facial recognition, which is the art of matching faces in pictures to identities. Image rec
From playlist Image Recognition
It’s true – some people hear colors, or taste words. But what produces synesthesia? Learn more at HowStuffWorks.com: http://science.howstuffworks.com/life/inside-the-mind/emotions/synesthesia.htm Share on Facebook: https://goo.gl/jowIii Share on Twitter: https://goo.gl/is4WqA Subscribe:
From playlist Our 5 Senses
What is the alternate in sign sequence
👉 Learn about sequences. A sequence is a list of numbers/values exhibiting a defined pattern. A number/value in a sequence is called a term of the sequence. There are many types of sequence, among which are: arithmetic and geometric sequence. An arithmetic sequence is a sequence in which
From playlist Sequences
Predicting outcomes with Pattern Recognition: Machine Learning for Algorithmic Trading p. 8
Using previous pattern outcomes to help us begin to predict future outcomes. Welcome to the Machine Learning for Forex and Stock analysis and automated trading tutorial series. In this series, you will be taught how to apply machine learning and pattern recognition principles to the field
From playlist Machine Learning for Forex and Stock analysis and algorithmic trading.
Sometimes you need to nest a pattern in another pattern. Learn how to build these patterns and then extract information from them. https://teacher.desmos.com/activitybuilder/custom/605e21d90925ca0c93fabbbd
From playlist Pattern Matching with Computation Layer
Analyzing Biomedical and Clinical Text with the Stanza Python NLP Library | Healthcare NLP Summit
Get your Free Spark NLP and Spark OCR Free Trial: https://www.johnsnowlabs.com/spark-nlp-try-free/ Register for NLP Summit 2021: https://www.nlpsummit.org/2021-events/ Watch all Healthcare NLP Summit 2021 sessions: https://www.nlpsummit.org/ The growing interest in biomedical and clini
From playlist Healthcare NLP Summit 2021
Stanza: A Multi-lingual Multi-domain Python Natural Language Processing Toolkit | NLP Summit 2020
Get your Free Spark NLP and Spark OCR Free Trial: https://www.johnsnowlabs.com/spark-nlp-try-free/ Register for NLP Summit 2021: https://www.nlpsummit.org/2021-events/ Watch all NLP Summit 2020 sessions: https://www.nlpsummit.org/ The growing availability of open-source natural languag
From playlist NLP Summit 2020
Kaggle Reading Group: Dissecting contextual word embeddings (Part 4) | Kaggle
Join Kaggle Data Scientist Rachael as she reads through an NLP paper! Today's paper is "Dissecting contextual word embeddings: Architecture and representation" (Peters et al, 2018). You can find a copy here: https://aclweb.org/anthology/D18-1179 unbalance: EDA,PCA,SMOTE,LR,SVM,DT,RF" by
From playlist Kaggle Reading Group | Kaggle
Text Analysis and Natural Language Processing Simplified | NLP Training | Edureka
(NLP Certification Training: https://www.edureka.co/python-natural-language-processing-course) This video on "Text Analysis and Natural Language Processing" will provide you with in-depth knowledge of NLP, the different components of NLP and it's various applications in the industry along
From playlist Natural Language Processing (NLP) | NLTK with Python
Only very rarely do words occur in isolation. Rather, they are inserted into precisely defined syntactic contexts. This E-Lecture discusses the principles of lexical insertion from categorization to the definitin of the argument structure of lexemes.
From playlist VLC206 - Morphology and Syntax
Natural Language Processing: Crash Course Computer Science #36
Today we’re going to talk about how computers understand speech and speak themselves. As computers play an increasing role in our daily lives there has been an growing demand for voice user interfaces, but speech is also terribly complicated. Vocabularies are diverse, sentence structures c
From playlist Computer Science
Python - Information Extraction Part 1 (2023 New)
Lecturer: Dr. Erin M. Buchanan Spring 2023 https://www.patreon.com/statisticsofdoom In this video, you will learn about information extraction: keyphrase extraction, named entity recognition/disambiguation, and relation extraction. You will learn about spacy, textacy, and more python p
From playlist Natural Language Processing
02.3 - ISE2021 - NLP Applications
Information Service Engineering 2021 Prof. Dr. Harald Sack Karlsruhe Institute of Technology Summer semester 2021 Lecture 2: Natural Language Processing - 1 2.3 - NLP Applications - OCR - Spell Checking and Word Prediction - Information Retrieval - Text categorization and summarization -
From playlist ISE 2021 - Lecture 02, 21.04.2021
Neural Models for Speech and Language: Successes, Challenges, and the... - Michael Collins
Workshop on Theory of Deep Learning: Where next? Topic: Neural Models for Speech and Language: Successes, Challenges, and the Relationship to Computational Models of the Brain Speaker: Michael Collins Affiliation: Columbia University Date: October 16, 2019 For more video please visi
From playlist Mathematics
Lecture 3 | GloVe: Global Vectors for Word Representation
Lecture 3 introduces the GloVe model for training word vectors. Then it extends our discussion of word vectors (interchangeably called word embeddings) by seeing how they can be evaluated intrinsically and extrinsically. As we proceed, we discuss the example of word analogies as an intrins
From playlist Lecture Collection | Natural Language Processing with Deep Learning (Winter 2017)
Pattern Matching - Being Flexible
As your patterns become more complex you'll need to build patterns that can match expressions with different but similar forms. Activity Link: https://teacher.desmos.com/activitybuilder/custom/60626999811e664d596ece18
From playlist Pattern Matching with Computation Layer
Emergent linguistic structure in deep contextual neural word representations - Chris Manning
Workshop on Theory of Deep Learning: Where next? Topic: Emergent linguistic structure in deep contextual neural word representations Speaker: Chris Manning Affiliation: Stanford University Date: October 15, 2019 For more video please visit http://video.ias.edu
From playlist Mathematics