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- Linear Regression Ethan Fetaya, James Lucas and Emad Andrews
- Statistical Modeling: The
- Applications of the Upside-Down Normal Loss Function
- Lecture 2: Linear Regression
- The Bias-Variance Decomposition 65
- Solutions to the Exercises of Section 1.8. 1.8.1. E(Z − B )2 = Var(Z) + (EZ
- The Fundamental Nature of the Log Loss Function
- Loss Functions for Multiset Prediction
- Some Thoughts About the Design of Loss Functions
- A Statistical View of Deep Learning Part 2
- Geometry of Neural Network Loss Surfaces Via Random Matrix Theory
- IEOR 265 – Lecture 5 M-Estimators 1 Maximum Likelihood Estimators
- Classification and Regression with Breiman Random Forests
- Variance and Bias for General Loss Functions
- L DMI: a Novel Information-Theoretic Loss Function for Training Deep Nets Robust to Label Noise
- Effects of Nonlinearity and Network Architecture on the Performance of Supervised Neural Networks
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- Maximum Likelihood Modeling
- Improving Classification Performance of Softmax Loss Function Based On
- 2, Mean-Variance Loss for Deep Age Estimation from a Face
- Loss Functions and Estimation
- Wavelet Pooling for Convolutional Neural Networks
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- A Tunable Loss Function for Robust Classification
- Examples of Maximum Likelihood Estimation and Optimization in R Joel S Steele
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- Loss Functions for Binary Classification and Class
- 1 Basic Concepts 2 Loss Function and Risk
- Arxiv:1902.04981V1 [Stat.ML] 13 Feb 2019 Approaches to Unsupervised Deep Learning Based on Adversarial Networks Have Recently Been Proposed [13]
- Understanding Random Forests
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- Loss Functions in Time Series Forecasting
- 1 Introduction
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- A Loss Function Approach to Model Specification Testing and Its
- Proof That the Median Minimizes the Mean Absolute Deviation
- Learning Filters for the 2D Wavelet Transform
- Bias Plus Variance Decomposition for Zero-One Loss Functions
- Random Jittering Loss Function
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- Loss Functions for Preference Levels: Regression with Discrete Ordered Labels Jason D
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- Logistic Loss Function
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- MLE and MAP Fall 2019 Last Time: Maximum Likelihood Estimation (MLE)
- Customizable Asymmetric Loss Functions for Machine Learning-Based Predictive Maintenance
- A General and Adaptive Robust Loss Function
- A Loss Function Analysis for Classification Methods in Text Categorization
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- Course Introduction Probability, Statistics and Quality Loss HW#1 Presentations
- Loss Functions for Clustering in Multi-Instance Learning
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- Information Theoretic Guarantees for Empirical Risk Minimization with Applications to Model Selection and Large-Scale Optimization
- Lecture 14 — October 13 14.1 Robust Statistics
- On Logistic Regression: Gradients of the Log Loss, Multi-Class Classification, and Other Optimization Techniques
- Fuzzy C-Means Clustering Using Asymmetric Loss Function
- CS229T/STAT231: Statistical Learning Theory (Winter 2016) Contents
- Adaptive Wavelet Pooling for Convolutional Neural Networks
- Loss Function –How We Measure the Costs Due to Potential Forecast Error
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- Lecture 2: Statistical Decision Theory Lecturer: Jiantao Jiao Scribe: Andrew Hilger
- Data Science 100
- Regression Models and Loss Functions Arxiv:1211.1043V1
- Objective Bayesian Methods for Estimation and Hypothesis Testing
- Loss Functions Modulate the Optimal Bias-Variance Trade-Off Adam Bear ([email protected]) Department of Psychology, Harvard University Cambridge, MA 02143 USA
- Learning Filter Widths of Spectral Decompositions with Wavelets
- Wavelet-Integrated Deep Networks for Single Image Super-Resolution
- Loss Function Based Ranking in Two-Stage, Hierarchical Models
- Logistic Regression