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 20 of 28 for “"Overfit"”.
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Maintaining the utility of learned knowledge using model-based adaptive control
The overfit problem in empirical learning and the utility problem in analytical learning both describe a common behavior of machine learning methods: the eventual degradation of performance due to increasing amounts of learned knowledge. Plotting the performance of the changing knowledge during …
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The Low-rank Simplicity Bias in Deep Networks
… recent work has asked: why do deep networks not overfit to their training data? In this work, we make a series of empirical observations that investigate and extend the hypothesis that deeper networks are inductively biased to find solutions with lower effective rank embeddings. We conjecture …
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Methodological advancements for improving performance and generating ensemble ecological niche models
… of occurrence data with the aim of reducing overfitting to sampling bias in ecological niche models (ENMs). Sampling bias in geographic space leads to localities that may also be biased in environmental space. If so, the model can overfit to those biases. As a preliminary test addressing this …
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Robust learning and segmentation for secure understanding
… at the same time. However, in general, they overfit and perform poorly on data sets that contain many features, but few examples. A novel stochastic regularization technique is presented, based on enhancing data sets with corrupted copies of the examples to produce a more robust classifier. …
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Influence modeling of complex stochastic processes
… more stable to outliers, and less likely to overfit. It can be used to study the interaction of over 100 persons and get good results.
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Improving Neural Network Classification Training
… the network as it trains in order to avoid local overfit and premature weight saturation. PL1 is a point-wise local binning algorithm used to calibrate a learning model to output more accurate posterior probabilities. This algorithm is used to improve the reliability of classification-based …
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Understanding Representations and Reducing their Redundancy in Deep Networks
… others. However, big deep neural networks still overfit very easily. This section proposes a new regularizer called DeCov, which leads to significantly reduced overfitting (difference between train and val performance) and greater generalization, sometimes better than dropout and other times not. …
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Fitting a Linear Regression Model and Forecasting in R in the Presence of Heteroskedascity with Particular Reference to Advanced Regression Technique Dataset on kaggle.com.
… making sure to avoid an overly complex or overfit model. Noting that our model suffers from unconditional heteroskedasticity, we discuss its goodness of fit. Then we use the model to predict sales prices for the point in the testing data set.</p>
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Comparison of Logistic Regression and Classification Trees to Forecast Short Term Defaults on Repeat Consumer Loans
… with classification trees is its tendency to overfit data eroding its robustness, making it vulnerable to underlying population characteristic shifts. Classification trees remains a popular technique due to its ease of application (algorithm machine learning basis) and model interpretation. …
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Improving Data-Driven Contact Localization and Force Estimation for Barometric Tactile Sensors
… more expressive models such as LSTMs tend to overfit on the collected data and physical phenomena observed during deployment were not well represented by the training metrics. To better understand the extent that these data-driven methods alone can improve sensor performance, I shift focus …
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High-dimensional machine learning for drug target discovery and precision medicine
… of samples, which makes models highly likely to overfit the training data (high-dimensionality). Second, there are technical and/or experimental confounders in any one study that make the features learned from an individual expression dataset not necessarily generalizable to other datasets …
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Convolutional Conditional Neural Processes
… where neural networks would traditionally overfit. Neural processes can produce well-calibrated uncertainties, effectively deal with missing data, and are simple to train. These properties make this family of models appealing for a breadth of applications areas, such as healthcare or …
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Achieving Robustness and Generalization in MARL for Sequential Social Dilemmas through Bilinear Value Networks
… trained through self-play typically converge and overfit to a singular Nash equilibrium. Consequently, these agents are limited to executing the specific strategy they have converged to during training, which renders them ineffective when faced with opponents employing commonly-used strategies …
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Toward automatic model adaptation for structured domains
… and simplicity, so that the model does not overfit to irrelevant characteristics of the training data. The optimal model is not only a function of the task to which it is applied, but also the amount of training data available. Copious training data can justify a complex model that includes …
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Bayesian Learning for Data-Efficient Control
… use deterministic models, which easily overfit data, especially small datasets. We use probabilistic Bayesian modelling to learn systems from scratch, similar to the PILCO algorithm, which achieved unprecedented data efficiency in learning control of several benchmarks. We extend PILCO …
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Overcoming Vulnerabilities of Machine Learning in Neuroimaging Applications
… machine learning, without which models tend to overfit and generalize poorly to new data. I propose a method to perform data augmentation, where additional data is simulated by transforming existing data, for 4D fMRI timeseries. Using this augmentation method, I develop predictors of …
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Novel optimizers for multi-objective deep learning
… of the underlying problem and causes models to overfit to a particular objective. Additionally, with a single network parameter (weight configurations), the Adam optimizer operates without independent searching agents. Therefore, the global search is not permissible as multiple agents do not …
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DETECTING DISTRIBUTED DENIAL OF SERVICE ATTACKS IN IPV6 BY USING ARTIFICIAL INTELLIGENCE TECHNIQUES
… capabilities, with high accuracy rate, does not overfit, has low computational costs, and uses less time for model development and attack detection.
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Glacial stratigraphy of northwestern Cass County, North Dakota
… each of these areas include eskers, abandoned or overfit river channels, transverse ridges, ice thrust masses, kettles and beach deposits.</p> <p>Seven lithostratigraphic units were identified on the basis of texture, very coarse sand lithology, matrix calcite-dolomite composition, clay …
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Choice Modeling and Assortment Optimization on the Transformer Model
… assortments. To ensure that our model does not overfit on the training data, we use dropout as the regularization method during training. We compare our model to both traditional choice models (the multinomial logit model and its synergistic variant that considers cross-product interaction) and …
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