Knowledge Representation and Machine Learning
Cognitive agents and machine learning by example : representation with conceptual graphs
... store knowledge acquired from raw ...actual representation of knowledge is the conceptual graph (CG) model of Sowa ...the representation and relations through labelled edges, their ...
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9. ARTIFICIAL INTELLIGENCE AND MEDICAL SCIENCE:A SURVEY
... categories: knowledge representation systems and machine learning ...systems. Knowledge representation systems, also known as expert systems, provide a structure for capturing ...
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Yang, Yinchong (2018): Enhancing representation learning with tensor decompositions for knowledge graphs and high dimensional sequence modeling. Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik
... the machine learning model would also implicitly detect anomalous prescriptions, by checking whether the actual prescriptions are among the top- n ranked recommendations made by the machine ...
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Study on Machine Learning and Deep Learning Methods for Human Action Recognition
... Local Representation: Local Representation identifies the local regions having salient motion ...for knowledge about human body model or people ...
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A generating technique and knowledge representation of multiple-answer problems for learning with solving knowledge
... pressing knowledge in each respective field and then to create knowledge expressions that completely express the problems’ ...such knowledge is non-essential work in terms of the person solving the ...
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Development and representation of the knowledge acquired during incidental sequence learning
... that learning was not based on response ...sequence learning is independent of the actual motor responses, response times in the transfer phase may have been speeded up by a factor that is unrelated to ...
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Novel Ensemble Approaches To Model Macroscopic Material Behavior Using Micromechanical Simulations
... The above diagram depicts on the graphical representation of the various machine learning ensemble algorithms and their Root Mean Square Errors. the CVParameterSelection classifier has 2.5 Root Mean ...
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Knowledge Driven Paper Recommendation Using Heterogeneous Network Embedding Method
... a learning representation of nodes using the word2vec model on node sequences experimented through random ...learn Knowledge network embed- dings to perform in recommendation ...a knowledge ...
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Techniques of Brain Cancer Detection from MRI using Machine Learning
... supervised learning technique. Learning take place by changing connection weights after each piece of data is processed, based on the amount of error in the target output as compared to the expected ...the ...
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Representation Learning with Ordered Relation Paths for Knowledge Graph Completion
... tion from relation paths between entity pairs is helpful for link prediction. Note that OPTransE outperforms baselines which do not take relation paths into consideration in most cases. These re- sults demonstrate the ...
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Cancer Prediction and Prognosis Using Machine Learning Techniques
... various machine learning techniques for different type of cancer prediction and prognosis (Breast Cancer, Lung Cancer, ...different machine learning techniques was to find out the most optimal ...
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Musculoskeletal Physiotherapy using Artificial Intelligence and Machine Learning
... It is observed that technology in the field of medical has been proven very beneficial. Varied amount of applications are being developed which are making life of doctors as well as patients easier. Technologies such as ...
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Learning Using Privileged Information: Similarity Control and Knowledge Transfer
... existing machine learning paradigm considers a simple scheme: given a set of training examples, find, in a given set of functions, the one that approximates the unknown decision rule in the best possible ...
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Learning and Knowledge Transfer with Memory Networks for Machine Comprehension
... for Knowledge Transfer While joint-training methods offer knowledge transfer by exploiting similarities and regularities across different tasks or datasets, the asymmet- ric nature of transfer and skewed ...
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Machine Learning and Data Mining Lecture Notes - Free Computer, Programming, Mathematics, Technical Books, Lecture Notes and Tutorials
... We now discuss an influential and effective classification algorithm called Support Vector Ma- chines (SVMs). In addition to their successes in many classification problems, SVMs are respon- sible for introducing and/or ...
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Knowledge visualization by Doctus Knowledge Galaxy
... the Knowledge Galaxy, the highest are the topics; each topic contains keywords; the keywords are described by attributes; and attributes have their places to be found, we call them ...galaxy. Knowledge ...
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On machine learning and knowledge organisation in Multimedia Information Retrieval
... in machine-learning methods such as machine vision algorithms (Karpathy and Li, 2015) have provided the functionality to identify specific objects in images, giving multimedia IR designers and ...
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Knowledge Representation and Machine Translation
... KNOWLEDGE REPRESENTATION AND MACHINE TRANSLATION COLING 82, J Horeck?) fed ) North Holland Publishing Company (~ Arademia 1982 KNOWLEDGE REPRESENTATION AND MACHINE TP~ANSLATION Susumu Sawai, Hiromichi[.] ...
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Advanced Machine Learning Approach: Deep Learning
... Due to the deep learning approach, the efficiency of image recognition and object detection has increased dramatically over the past seven years. Convolutional neural networks (CNNs) gave the computer vision field ...
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