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.
Results
Showing 1 to 20 of 109 for “"Binary classification"”.
-
Binary classification with training under both classes
This thesis focuses on the binary classification problem with training data under both classes. We first review binary hypothesis testing problems and present a new result on the case of countably infinite alphabet. The goal of binary hypothesis testing is to decide between the two underlying …
-
Inductive logic programming with gradient descent for supervised binary classification
… logic programming technique for supervised binary classification. We start by developing our methodology for binary input data, and then extend the approach to numerical data using a threshold-gate based binarization technique. We test our implementations on datasets with varying pattern …
-
Novel low-complexity MIMO detection based on constellation shift binary classification
… MIMO detection known as Constellation Shift Binary Classification (CSBC). The proposed method utilizes the constellation structure to deduce a constellation shift that is used to reduce the problem of detecting the symbol into binary classification. The proposed method is proven to outperform …
-
Pattern recognition of brain fMRI images for various physiological states
… experimental conditions were used involving both binary and multi-class classification. Bilateral finger tapping data which had two distinct states "Active" and "Rest" were used for binary classification. Binary classification was done using Learning Vector Quantization (LVQ) and Least Square …
-
Development of a Bagging-based Ensemble Model for ECG Classification
… learning models in cardiovascular disease classification and recognition, is rapidly growing. CNN, LSTM, and Transformer models have demonstrated in various studies that, when implemented with robust architectures and supported by ample datasets, they can achieve highly accurate results. …
-
Apklausų dalyvių aktyvumo analizė, pritaikant įvairius binarinio klasifikavimo algoritmus /
… The aim of this study was to test various binary classification algorithms to determine whether a survey response would be of high quality or not. To achieve this goal, five binary classification algorithms were selected: logistic regression, K-nearest neighbors, decision tree, support …
-
Incorporating Three-Way Email Spam Filtering With Game-Theoretic Rough Sets
Email spam filtering commonly is viewed as binary classification problem, that is, classifies incoming email messages into spam or non-spam email. But it has two main limitations. Firstly, binary classification needs people to make definite decisions that are hard. Secondly, binary classification …
-
High Dimensional Analysis of Genetic Data for the Classification of Type 2 Diabetes Using Advanced Machine Learning Algorithms
… epistatic interactions in T2D genetic data for binary classification tasks. This framework includes traditional GWAS quality control, association analysis, deep learning stacked autoencoders, and a multilayer perceptron for classification. Quality control procedures are conducted to exclude …
-
Non-asymptotic bounds for prediction problems and density estimation.
… first part answers some open questions for the binary classification problem in the framework of active learning. Given a random couple (X,Y) with unknown distribution P, the goal of binary classification is to predict a label Y based on the observation X. Prediction rule is constructed from a …
-
Clinical Interpretation of Novel Copy Number Variations
… of uncharacterized CNVs as a multiple instance binary classification problem. We analyze the current state of clinical techniques, then present and test several novel statistical approaches to the problem.
-
Psychological Understanding of Textual journals using Natural Language Processing approaches
… dataset. The first chapter is related to binary classification on a personality detection dataset, while the second one is about sentiment analysis and Topic Modeling of sleep-related reports.
-
An end-to-end grading neural network for middle-school math problems
… of the human grader. The task is formulated as a classification problem. Given an answer key and a student's solution, the classifier needs to predict two metrics: (1) a four-class classification result that measures the completeness of the student's detailed steps and (2) a binary classification …
-
Computational support for media ecosystems research
… pipeline, enabling novices to train their own binary classification models to detect the presence of a specific frame within the text of a news story. The visualization and interface were evaluated in a user study and think-aloud test respectively. These tools were developed for integration …
-
Calibration, feature extraction and classification of water contaminants using a differential mobility spectrometer
… approach was developed for both chemical type classification and concentration classification of water contaminants for FAIMS signals. The three steps in this approach are calibration, feature extraction, and classification. Calibration was carried out to remove baseline fluctation and other …
-
Coreference resolution on entities and events for hospital discharge summaries
… summary. We treat coreference resolution as a binary classification problem. Our approach yields insights into the critical features for coreference resolution for entities that fall into five medical semantic categories that commonly appear in discharge summaries.
-
Lexical Aspectual Classification
This work is a first attempt at classification of Lexical Aspect. In this dissertation I describe eight lexical aspectual classes, each initially containing a few members. Using distributional analysis I generate 132 additional seeds, each of which was approved by at least seven out of nine judges. …
-
Large Language Model Routing with Benchmark Datasets
… the “router” model will solve a collection of binary classification tasks. This work will demonstrate the utility and limitations of learning model routers from various benchmark datasets, where performance is improved upon using any single model for all tasks.
-
Artificial intelligence in business analytics, capturing value with machine learning applications in financial services
… decision making. The focus is on supervised binary classification on structured datasets, which are vastly present in relational databases across all enterprises. Advanced analytics has become indispensable for today's corporate world and it is demonstrated that predictive analytics is one of …
-
Automatic Classification and Segmentation of Patterned Martian Ground Using Deep Learning Techniques
… the use of deep learning techniques in the classification of Martian polygonally patterned ground from HiRISE images. Three tasks are considered, a binary classification to identify images containing polygons, multiclass classification distinguishing different polygon types and semantic …
Page 1 of 6