Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 14 of 14 for “"linear classification"”.
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Financial prediction using non linear classification techniques
… thesis, we explore the ability of statistical classification methods to predict financial events in the bond and stock markets. Our classification methods include conventional Linear Dicriminant Analysis (LDA), and a number of less familiar non-linear techniques such as Probabilistic Neural …
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Accounting for Additional Heterogeneity: A Theoretic Extension of an Extant Economic Model
… other data sets. This includes examples of using linear classification, fitting baseline-category logit models, and running the genetic algorithm.</p>
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Predicting drug - target interaction network using deep learning models for drug repurposing through genetic information
… logistic regression and LASSO-based regularized linear classification models to predict drug-target interactions, which were used for drug repurposing for inflammatory bowel disease and breast cancer. Experiments showed that the model over performed than traditional logistic regression models and …
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Seasonality of circulation in southern Africa using the Kohonen self-organising map
A technique employing the classification capabilities of the Kohonen self-organising map (SOM) is introduced into the body of computer-based techniques available to synoptic climatology. The SOM is one of many types of artificial neural networks (ANN) and is capable of unsupervised learning or …
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Classification with Large Sparse Datasets: Convergence Analysis and Scalable Algorithms
… of a product or the age group of a user, etc. Linear classifiers are popular choices for classifying such datasets because of their efficiency. In order to classify the large sparse data more effectively, the following important questions need to be answered. 1. Sparse data and convergence …
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Simple linear classifiers via discrete optimization : learning certifiably optimal scoring systems for decision-making and risk assessment
Scoring systems are linear classification models that let users make quick predictions by adding, subtracting, and multiplying a few small numbers. These models are widely used in applications where humans have traditionally made decisions because they are easy to understand and validate. In spite …
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Stock Option Valuations and Constraint Enforcement Using Neural Networks
… have long been studied, being inherently non-linear financial derivatives. These instruments have a ubiquitous presence in institutional investment practice, and present many favourable and unique benefits to an investment portfolio. Neural Networks on the other hand have become a more …
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Scalable sparsity structure learning using Bayesian methods
… be directly applied to the high dimensional linear classification. In theory, we not only build a bridge to connect the estimation error of the mean difference and the classification error in different scenarios, also provide sufficient conditions of sub-optimal classifiers and optimal …
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Informing the next generation of auditory midbrain implants, neuronal population dynamics in the auditory cortex and midbrain, and the potentials of optogenetic stimulation
… as opposed to spontaneous activity alone. Using linear classification analysis, a spike rate code was generally found to be sufficient for distinguishing between natural sound stimuli, in both the AC and IC. However, the IC achieved comparable performance to the AC using fewer single ormulti …
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Linear and ellipsoidal pattern separation: theoretical aspects and experimental analysis
This thesis deals with a pattern classification problem, which geometrically implies data separation in some Euclidean feature space. The task is to infer a classifier (a separating surface) from a set or sequence of observations. This classifier would later be used to discern observations of …
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Fabrication and Application of a Polymer Neuromorphic Circuitry Based on Polymer Memristive Devices and Polymer Transistors
… even a single neuron, are capable of performing linear classification for a real-life problem.</p>
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Effects of Sampling Sufficiency and Model Selection on Predicting the Occurrence of Stream Fish Species at Large Spatial Extents
… of seven species in each of three regions using linear discriminant function, generalized linear, classification tree, and artificial neural network statistical models. I also assess the efficacy of stream classification methods for predicting species occurrence. No modeling method proved …
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Topic Identification from Spoken TED-Talks
… konkrétne Multinomial Naive Bayes a Linear Support Vector Machines, kde druhá technika dosiahla vyššiu presnosť klasifikácie.