NDLTD Global ETD Search
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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.

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Showing 421 to 430 of 981 (0.015 seconds)
421

Move to the Head of the Class: Teacher Agency in Constructing Student Roles in a Rural Elementary School

Bukky, Molly B. 29 July 2008 (has links)
No description available.
Sociology
Reproduction Theory
Hidden Curriculum
Rural Education
Sociology
symbolic construction of social class
parental involvement
422

Hierarchical video semantic annotation – the vision and techniques

Li, Honglin January 2003 (has links)
No description available.
Video Annotation
Hierarchical Structure
Video Query
Syntactic Pattern Analysis
Hidden Markov Model
Video Markup Language
423

Information flow in a fragmented dealer market: three essays on price discovery

Tuttle, Laura A. 30 September 2004 (has links)
No description available.
Business Administration, General
market microstructure
price discovery
electronic communication network
ECN
hidden orders
424

Stochastic models for MRI lesion count sequences from patients with relapsing remitting multiple sclerosis

Li, Xiaobai 14 July 2006 (has links)
No description available.
Statistics
lesion
relapsing remitting multiple sclerosis
longitudinal count data
queueing theory
hidden Markov models
425

Understanding the connectionist modeling of quasiregular mappings in reading aloud

Kim, Woojae 26 February 2007 (has links)
No description available.
Quasiregular mappings
Word naming
Reading aloud
Connectionist model
Hidden unit representation
426

Three Essays on Information Transmission and Pooling in Common Value Decision Making

Lightle, John P. 24 June 2008 (has links)
No description available.
Economics
group decision making
expert advice
hidden profile
biased recall
biased communication
427

Assessment of Carbon Sequestration in the U.S. Residential Landscape

Zirkle, Gina N. 20 August 2010 (has links)
No description available.
Agriculture
Soil Sciences
Turfgrass managment
C sequestration
Hidden carbon costs
Carbon equivalents
lawns
428

A HMM Approach to Identifying Distinct DNA Methylation Patterns for Subtypes of Breast Cancers

Xu, Maoxiong 21 July 2011 (has links)
No description available.
Bioinformatics
Computer Science
Breast Cancer
DNA methylation
Gene Expression
Hidden Markov Model
429

Student Engagement for College Students with the Hidden Disability of Orthostatic Intolerance

Karabin, Beverly Lynn January 2009 (has links)
No description available.
Education
Health Care
Higher Education
Nursing
Rehabilitation
student engagement
college students
hidden disability
orthostatic intolerance
430

Using a Hidden Markov Model as a Financial Advisor

Lindqvist, Emil, Andersson, Robert January 2021 (has links)
People have been trying to predict the stock marketsince its inception and financial investors have made it theirprofession. What makes predicting the stock market such ahard task is its seemingly random dependency on everythingfrom Elon Musks tweets to future earnings. Machine learninghandles this apparent randomness with ease and we will try itout by implementing a Hidden Markov Model. We will modeltwo different stocks, Tesla, Inc. and Coca-Cola Company, andtry using the forecasted prices as a template for a simple tradingalgorithm. We used an approach of calculating the log-likelihoodof preceding observations and correlated it with the log-likelihoodof all the preceding subsequences of equivalent size by turningthe time window by one day in the past. The results show thatmodeling two stocks of different volatility is possible, but usingthe result as a template for trading came back inconclusive withless than 50 percent successful trades for both of the modelledstocks. / Människor har försökt förutsäga aktiemarknaden sedan starten och finansiella investerare har gjort det till sitt yrke. Det som gör att förutsäga aktiemarknaden till en så svår uppgift är dess till synes slumpmässiga beroende av allt från Elon Musks tweets till framtida intäkter. Maskininlärning hanterar denna uppenbara slumpmässighet med lätthet och vi kommer att testa det genom att implementera en Hidden Markov-modell. Vi kommer att modellera två olika aktier, Tesla, Inc. och Coca-Cola Company, och försöka använda de prognostiserade priserna som bas för en enkel algoritm att handla på. Vi använde ett tillvägagångssätt för att beräkna log-sannolikheten för föregående observationer och korrelerade den med logsannolikheten för alla föregående följder av motsvarande storlek genom att vrida tidsfönstret med en dag tidigare. Resultaten visar att det är möjligt att modellera två aktier med olika volatilitet, men att använda resultatet som en mall för handel kom tillbaka de med mindre än 50 procent framgångsrika affärer för båda modellerna. / Kandidatexjobb i elektroteknik 2021, KTH, Stockholm
hidden markov models
stock market prediction
Elektroteknik och elektronik

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