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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 20 of 171 for “"classification models"”.
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Discriminative Classification Models for Internet Security
… for legitimate users often requires the classification of entities as malicious or legitimate in order to initiate countermeasures. As an example, inbound email spam filters decide for spam or non-spam. They can base their decision on both the content of each email as well as on features …
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Statistical Inference for Diagnostic Classification Models
Diagnostic classification models (DCM) are an important recent development in educational and psychological testing. Instead of an overall test score, a diagnostic test provides each subject with a profile detailing the concepts and skills (often called "attributes") that he/she has mastered. …
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Simplifying the usage and construction of deep image classification models
La Inteligencia Artificial, y en concreto el Aprendizaje Profundo (en inglés Deep Learning), ha cobrado gran importancia en los últimos años debido al rápido aumento de la capacidad de procesamiento, a la disponibilidad de una gran cantidad de datos y al surgimiento de diferentes librerías de …
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Contributions to evaluation of machine learning models. Applicability domain of classification models
… problems. The performance of machine learning models depends on algorithms and the data. Moreover, learning algorithms create a model of reality through learning and testing with data processes, and their performance shows an agreement degree of their assumed model with reality. ML algorithms …
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Contributions to evaluation of machine learning models. Applicability domain of classification models
… problems. The performance of machine learning models depends on algorithms and the data. Moreover, learning algorithms create a model of reality through learning and testing with data processes, and their performance shows an agreement degree of their assumed model with reality. ML algorithms …
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Learning classification models of cognitive conditions from subtle behaviors in the digital Clock Drawing Test
… machine learning methods to build prediction models that achieve high accuracy. We operationalized widely used existing scoring algorithms so that we could use them as benchmarks for our models. We worked with clinicians to define guidelines for model interpretability, and constructed sparse …
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The Detection and Characterization of Severe Features in Colonoscopy Videos Using Combined Segmentation and Classification Models
… smaller components and combines segmentation and classification models to characterize IBD features and predict disease severity for frame-level and clip-level data. Our combined segmentation and classification models had an average accuracy of 90% for the detection of severe IBD features such as …
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Empowering novices to understand and use machine learning with personalized image classification models, intuitive analysis tools, and MIT App Inventor
… core machine learning concepts with image classification, one of the most basic and widespread examples of machine learning. I built a web interface that allows users to train and test personalized image classification models on pictures taken with their computers--webcams. Furthermore, I …
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Legislative Language for Success
… feature extraction, implementation of classification models, and feature analysis. Several features were extracted and tested to find those that had the greatest impact on the bill outcome. The features chosen provided information on the sentence complexity and type of words used …
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Pattern recognition and the nondeterminable affine parameter problem
… implementing pattern recognition systems using classification models such as artificial neural networks (ANNs) and algorithms whose theoretical foundations come from statistics. The issues involved in implementing several classification models and pre-processing operators - that are applied to …
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STUDYING PRODUCT REVIEWS USING SENTIMENT ANALYSIS BASED ON INTERPRETABLE MACHINE LEARNING
… (rule-based) and black-box opaque (BERT) models. We find that while the black-box model is more correlated with product ratings, there are interesting counterexamples where the sentiment analysis results by the glass-box model are better aligned with the rating. Next, we explore how well …
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WAVELENGTH AND VISUAL FEATURE SELECTION FOR NON-DESTRUCTIVE EVALUATION OF FUSARIUM HEAD BLIGHT IN WHEAT USING HYPERSPECTRAL IMAGING
… acquisition and evaluates an extensive range of classification models trained on spatial–spectral features. The scanner captured hyperspectral scans of over 1,500 healthy and Fusarium-damaged kernels, from which statistical features were extracted at each wavelength. Classification models were …
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Deep Learning for Early Detection, Identification, and Spatiotemporal Monitoring of Plant Diseases Using Multispectral Aerial Imagery
… the development of automatic and accurate image classification systems. These advances coupled with the widespread availability of multispectral aerial imagery provide a cost-effective method for developing crop-diseases classification tools. However, large datasets are required to train deep …
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Weakly-supervised text classification
… increasing popularity for the classic text classification task, due to their strong expressive power and less requirement for feature engineering. Despite such attractiveness, neural text classification models suffer from the lack of training data in many real-world applications. Although …
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Online tool condition monitoring for ultrasonic metal welding via sensor fusion and machine learning
… were selected and subsequently used by ML based classification models. A variety of classification models were trained and tested using experimental data. The best model achieved consistent prediction accuracy of close to 100%. The proposed TCM system not only provides real-time TCM for UMW but …
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Applying Supervised Machine Learning Techniques to Municipal Bond Trading
… decisions. The paper will examine a variety of classification models trained in a supervised environment. The paper will discuss: i. How to prepare data for machine learning analysis ii. The basic mathematical concepts of each model iii. The results of each model and how to interpret them iv. …
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Classification trees outperform logistic regression predictions of attrition in the U.S. Marine Corps
… compared the performance of machine learning classification models against logistic regression in the context of predicting training attrition from the Delayed Enlistment Program in the United States Marine Corps (UMSC) with scores from the Tailored Adaptive Personality Assessment System …
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Design and Implementation of Faculty Support System to Reduce Course Dropout Rates
… System (FSS) is proposed that learns different classification models to predict student course performance based on his/her attendance, and performance in assignments, quizzes, in-class group projects, and exams. The investigated approaches for this task include Naïve Bayes, Multi-Layer Neural …
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Application of machine learning methods for design of crystallisation processes
… solvents. Based on these experimental data, a ML classification model was constructed for predicting the crystallisation outcomes and crystal habit of paracetamol with ~77.78 % prediction accuracy.;Analysis of the ML model revealed that the physicochemical descriptors and predictive capabilities …
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