Global ETD Search

371	Transformer-based Model for Molecular Property Prediction with Self-Supervised Transfer Learning Lin, Lyu January 2020 (has links) Molecular property prediction has a vast range of applications in the chemical industry. A powerful molecular property prediction model can promote experiments and production processes. The idea behind this degree program lies in the use of transfer learning to predict molecular properties. The project is divided into two parts. The first part is to build and pre-train the model. The model, which is constructed with pure attention-based Transformer Layer, is pre-trained through a Masked Edge Recovery task with large-scale unlabeled data. Then, the performance of this pre- trained model is tested with different molecular property prediction tasks and finally verifies the effectiveness of transfer learning.The results show that after self-supervised pre-training, this model shows its excellent generalization capability. It is possible to be fine-tuned with a short period and performs well in downstream tasks. And the effectiveness of transfer learning is reflected in the experiment as well. The pre-trained model not only shortens the task- specific training time but also obtains better performance and avoids overfitting due to too little training data for molecular property prediction. / Prediktion av molekylers egenskaper har en stor mängd tillämpningar inom kemiindustrin. Kraftfulla metoder för att predicera molekylära egenskaper kan främja vetenskapliga experiment och produktionsprocesser. Ansatsen i detta arbete är att använda överförd inlärning (eng. transfer learning) för att predicera egenskaper hos molekyler. Projektet är indelat i två delar. Den första delen fokuserar på att utveckla och förträna en modell. Modellen består av Transformer-lager med attention- mekanismer och förtränas genom att återställa maskerade kanter i molekylgrafer från storskaliga mängder icke-annoterad data. Efteråt utvärderas prestandan hos den förtränade modellen i en mängd olika uppgifter baserade på prediktion av molekylegenskaper vilket bekräftar fördelen med överförd inlärning.Resultaten visar att modellen efter självövervakad förträning besitter utmärkt förmåga till att generalisera. Den kan finjusteras med liten tidskostnad och presterar väl i specialiserade uppgifter. Effektiviteten hos överförd inlärning visas också i experimenten. Den förtränade modellen förkortar inte bara tiden för uppgifts-specifik inlärning utan uppnår även bättre prestanda och undviker att övertränas på grund otillräckliga mängder data i uppgifter för prediktion av molekylegenskaper. Computer and Information Sciences Data- och informationsvetenskap
372	AIRS: a Resource Limited Artificial Immune Classifier Watkins, Andrew B 14 December 2001 (has links) The natural immune system embodies a wealth of information processing capabilities that can be exploited as a metaphor for the development of artificial immune systems. Chief among these features is the ability to recognize previously encountered substances and to generalize beyond recognition in order to provide appropriate responses to pathogens not seen before. This thesis presents a new supervised learning paradigm, resource limited artificial immune classifiers, inspired by mechanisms exhibited in natural and artificial immune systems. The key abstractions gleaned from these immune systems include resource competition, clonal selection, affinity maturation, and memory cell retention. A discussion of the progenitors of this work is offered. This work provides a thorough explication of a resource limited artifical immune classification algorithm, named AIRS (Artificial Immune Recognition System). Experimental results on both simulated data sets and real world machine learning benchmarks demonstrate the effectiveness of the AIRS algorithm as a classification technique. Supervised Learning AIS Machine Learning Biologically Motivated Computing Artificial Immune Systems Immunological Computation
373	Gravitropic Signal Transduction: A Systems Approach to Gene Discovery Shen, Kaiyu 12 June 2014 (has links) No description available. Biology Computer Science Systems Biology Semi-supervised machine learning Gravitropism Transcriptome analysis Biological network
374	Deep-learning Approaches to Object Recognition from 3D Data Chen, Zhiang 30 August 2017 (has links) No description available. Robotics Computer Science Nanoscience Medical Imaging deep learning 3D object recognition semi-supervised learning knowledge transfer
375	Rethinking Document Classification: A Pilot for the Application of Text Mining Techniques To Enhance Standardized Assessment Protocols for Critical Care Medical Team Transfer of Care Walker, Briana Shanise 09 June 2017 (has links) No description available. Bioinformatics Nursing critical care transfer of care text classification document classification supervised classification dialogue
376	Semi-supervised Ensemble Learning Methods for Enhanced Prognostics and Health Management Shi, Zhe 15 May 2018 (has links) No description available. Mechanical Engineering Regression Classification PHM Semi-supervised Ensemble Learning Unlabeled samples Insufficient training data set
377	AN ALL-ATTRIBUTES APPROACH TO SUPERVISED LEARNING VANCE, DANNY W. January 2006 (has links) No description available. machine learning supervised learning support vector machine k-nearest neighbors decision tree SVM KNN CART C4.5
378	Land Cover Change Across an Urban-Rural Transect in Southern Ohio, 1988-2008 Walsh, Steven 18 August 2009 (has links) No description available. Geography Ohio Land Cover Change Detection Supervised Classification Remote Sensing GIS
379	OPTIMAL FEATURE SUBSET SELECTION ALGORITHMS FOR UNSUPERVISED LEARNING WU, CHEN January 2000 (has links) No description available. sub-optimal supervised feature BFS (Breath First Search) DFS (Depth First Search)
380	Identification of Uniform Class Regions using Perceptron Training Samuel, Nikhil J. 15 October 2015 (has links) No description available. Computer Science Perceptron Training Uniform Class Regions Classification Supervised Learning Error Correction Linearly non-separable datasets

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