Showing 1 - 7 of 7 Items
Exploiting Context in Linear Influence Games: Improved Algorithms for Model Selection and Performance Evaluation
Date: 2022-01-01
Creator: Daniel Little
Access: Open access
- In the recent past, extensive experimental works have been performed to predict joint voting outcomes in Congress based on a game-theoretic model of voting behavior known as Linear Influence Games. In this thesis, we improve the model selection and evaluation procedure of these past experiments. First, we implement two methods, Nested Cross-Validation with Tuning (Nested CVT) and Bootstrap Bias Corrected Cross-Validation (BBC-CV), to perform model selection and evaluation with less bias than previous methods. While Nested CVT is a commonly used method, it requires learning a large number of models; BBC-CV is a more recent method boasting less computational cost. Using Nested CVT and BBC-CV we perform not only model selection but also model evaluation, whereas the past work was focused on model selection alone. Second, previously models were hand picked based on performance measures gathered from CVT, but both Nested CVT and BBC-CV necessitate an automated model selection procedure. We implement such a procedure and compare its selections to what we otherwise would have hand picked. Additionally, we use sponsorship and cosponsorship data to improve the method for estimating unknown polarity values of bills. Previously, only subject code data was used. This estimation must be done when making voting outcome predictions for a new bill as well as measuring validation or testing errors. We compare and contrast several new methods for estimating unknown bill polarities.
A Machine Learning Approach to Sector Based Market Efficiency
Date: 2023-01-01
Creator: Angus Zuklie
Access: Open access
- In economic circles, there is an idea that the increasing prevalence of algorithmic trading is improving the information efficiency of electronic stock markets. This project sought to test the above theory computationally. If an algorithm can accurately forecast near-term equity prices using historical data, there must be predictive information present in the data. Changes in the predictive accuracy of such algorithms should correlate with increasing or decreasing market efficiency. By using advanced machine learning approaches, including dense neural networks, LSTM, and CNN models, I modified intra day predictive precision to act as a proxy for market efficiency. Allowing for the basic comparisons of the weak form efficiency of four sectors over the same time period: utilities, healthcare, technology and energy. Finally, Within these sectors, I was able to detect inefficiencies in the stock market up to four years closer to modern day than previous studies.
Teaching Computers to Teach Themselves: Synthesizing Training Data based on Human-Perceived Elements
Date: 2019-05-01
Creator: James Little
Access: Open access
- Isolation-Based Scene Generation (IBSG) is a process for creating synthetic datasets made to train machine learning detectors and classifiers. In this project, we formalize the IBSG process and describe the scenarios—object detection and object classification given audio or image input—in which it can be useful. We then look at the Stanford Street View House Number (SVHN) dataset and build several different IBSG training datasets based on existing SVHN data. We try to improve the compositing algorithm used to build the IBSG dataset so that models trained with synthetic data perform as well as models trained with the original SVHN training dataset. We find that the SVHN datasets that perform best are composited from isolations extracted from existing training data, leading us to suggest that IBSG be used in situations where a researcher wants to train a model with only a small amount of real, unlabeled training data.
Selective Procedural Content Generation Using Multi-Discriminator Generative Adversarial Networks This record is embargoed.
- Embargo End Date: 2025-05-16
Date: 2024-01-01
Creator: Darien Gillespie
Access: Embargoed
Distance Based Pre-clustering for Deep Time-Series Forecasting: A Data Selection Approach This record is embargoed.
- Embargo End Date: 2025-05-16
Date: 2024-01-01
Creator: Leopold Felix Spieler
Access: Embargoed
An Investigation of Genetics-Based Machine Learning as Applied to Global Crop Yields Access to this record is restricted to members of the Bowdoin community. Log in here to view.
Date: 2017-05-01
Creator: William Gantt
Access: Access restricted to the Bowdoin Community
Ideal Point Models with Social Interactions Applied to Spheres of Legislation Access to this record is restricted to members of the Bowdoin community. Log in here to view.
Date: 2020-01-01
Creator: Luca Ostertag-Hill
Access: Access restricted to the Bowdoin Community