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 185 for “"Loss function"”.
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Novel Instance-Level Weighted Loss Function for Imbalanced Learning
… minimize the binary cross-entropy objective function to determine the final parameter estimates. This objective function assumes an equal class distribution between the minority (i.e. events) and majority (i.e. non-events) classes, which almost never exists in real-world modeling. In the …
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On Critical Care Data and Machine Learning Loss Function Landscapes
… models, and local minima of the neural network loss functions are found using basin-hopping methods implemented with the open source software GMIN. In this way, the landscape of the loss function defined by the neural network model can be explored. Area Under the Curve (AUC) values were used to …
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Imbalanced learning using actuarial modified loss function in tree-based models
… mass at zero and the heavy tail of insurance loss distribution poses the challenge to apply traditional methods directly to claim loss modeling. Via an illustrative simple dataset, this thesis first pinpoints the pitfall in the traditional tree-based algorithm’s splitting function. This thesis …
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Comparing GARCH models for gold price data, using a statistical loss function approach and an option pricing approach
Derivative instruments that rely on the price of gold are traded in large volumes. A significant number of these instruments are influenced by the volatility of gold price movements. Hence, it is important to understand the volatility of this commodity when developing successful trading and hedging …
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A Comparative Study of Machine Learning Models for Multivariate NextG Network Traffic Prediction with SLA-based Loss Function
… a custom Service-Level Agreement (SLA) - based loss function to ensure SLA violation constraints while minimizing overprovisioning. The proposed SLA-based parametric custom loss functions are used to maintain the SLA violation rate percentages the network operators require. Our approach is …
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Robustness of Consistent Loss Functions for Multinomial Outcome Models
… estimation, which uses the logarithmic loss function, is the default method used to estimate latent parameters consistently in multinomial outcome models. However, it is sensitive to even a tiny fraction of corruption in the training data. Alternatively, other loss functions in the …
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Completion of hinge loss has an implicit bias
A new loss function is proposed which learns the hinge loss function an infinite number of times pushing $f(x_i)y_i \to \infty$. It is proven that for a linear model on linearly separable data this modified hinge loss function converges in the direction of the $\ell_2$ max-margin separator at a …
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Nuclear norm penalized LAD estimator for low rank matrix recovery
… We study the framework using absolute deviation loss function and nuclear penalty. While nuclear norm penalty is widely utilized heuristic method for shrinkage to low rank solution, the absolute deviation loss function is rarely studied. We establish an near oracle optimal recovery bound and gave …
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Coherency Loss for Hierarchical Time Series Forecasting
… time series forecasts. We propose a new loss function, called Network Coherency Loss, that minimizes the coherency loss of the weight and bias of the final linear layer of a neural network. We compare it against a baseline without coherency and a state of the art method that uses …
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Optimal versus naive diversification : do different loss functions improve portfolio choice?
… different settings. In each setting, I vary the loss function used when estimating returns and covariances, length of the estimation window, and number of factors used in our estimation model. I find that when measuring performance by Sharpe ratio, choice of loss function strongly influences …
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Increasing the efficiency of multiple-use inventory procedures
… experience or a rule-of thumb, but rather a cost-loss function for multiple inventories and multiple decisions was minimized. The cost of collecting data will increase as sample size increases, while the expected monetary loss that will occur to the decision-maker from making decisions with …
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Segregation similarity loss in morphological ranking of image search in histopathology
… method suffer from bias, and the classification loss function cannot make it possible for us to reduce bias during the network’s training phase. Secondly, the research proposes a new loss function, Similarity Loss (SL), to address these problems. This loss function allows us to train the model …
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Uncertainty-Inclusive Contrastive Learning for Leveraging Synthetic Images
… Learning (UniCon), a novel contrastive loss function that incorporates uncertainty weights for synthetic images during training. Extending the framework of supervised contrastive learning, we add a learned hyperparameter that weights the synthetic input images per class, adjusting the …
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Robust methods for analyzing multivariate responses with application to time-course data
… of the least squares methods. We use Huber's loss function in developing robust methods for time-course multivariate responses. We use spline basis expansion of the time-varying regression coefficients to reduce dimensionality, and downweight the influence of outliers with Huber's loss …
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Admissibility ofp-value rules
… judged on the behavior of its p-value with the loss function being an increasing function G of the p-value.
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Asymmetric Loss Functions and Combination of Forecasts with Applications in Equity Premium Prediction
… methods, called the Discount Asymmetric Square Loss Function (DASLF), the Discount Linex Loss Function (DLLF), the Discount Lin-Lin Loss Function (DLLLF) and the Discount General Entropy Loss Function (DGELF) are proposed to maximize forecast accuracy and boost the efficiency of forecasts …
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Exploring Loss Functions in Machine Learning
<p>The loss function plays a critical role in machine learning. It is fundamental in training, evaluating, and optimizing machine learning models, directly impacting their effectiveness and efficiency in solving specific tasks. We explore three new loss functions and their applications. Softmax …
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Bandwidth Selection for Level Set Estimation in the Context of Regression and a Simulation Study for Non Parametric Level Set Estimation When the Density Is Log-Concave
… it controls the amount of smoothing for a function's estimator. Traditional methods for bandwidth selection involve optimizing a global loss function (e.g. least squares cross validation, asymptotic mean integrated squared error). Nevertheless, a global loss function becomes suboptimal for …
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On Machine Learning Loss Landscapes
Loss functions are pivotal to the training of every machine learning model. When evaluating the loss function over a large range of parameters, a loss landscape is obtained. In this thesis, loss landscapes for various classes of machine learning models are explored. Geometric properties of the loss …
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SCL: A Lattice-Based Approach to Infer Three-Dimensional Chromosome Structures from Single-Cell Hi-C Data
… based on single-cell Hi-C data. We designed a loss function and a 2D Gaussian function specifically for the characteristics of single-cell Hi-C data. A chromosome is represented as beads-on-a-string and stored in a 3D cubic lattice. Metropolis-Hastings simulation and simulated annealing are …
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