Pages that link to "Item:Q5959943"

The following pages link to Logistic regression, AdaBoost and Bregman distances (Q5959943):

Displaying 42 items.

• Tikhonov, Ivanov and Morozov regularization for support vector machine learning (Q285946) (← links)
• The information geometry of Bregman divergences and some applications in multi-expert reasoning (Q296320) (← links)
• Recovering occlusion boundaries from an image (Q408866) (← links)
• A noise-detection based AdaBoost algorithm for mislabeled data (Q454443) (← links)
• Affine invariant divergences associated with proper composite scoring rules and their applications (Q470075) (← links)
• A Fisher consistent multiclass loss function with variable margin on positive examples (Q887268) (← links)
• Putting objects in perspective (Q942844) (← links)
• Sketching information divergences (Q1009269) (← links)
• Surrogate maximization/minimization algorithms and extensions (Q1009342) (← links)
• Parallelizing AdaBoost by weights dynamics (Q1019879) (← links)
• Duality for Bregman projections onto translated cones and affine subspaces. (Q1874472) (← links)
• On the Bayes-risk consistency of regularized boosting methods. (Q1884602) (← links)
• Density-ratio matching under the Bregman divergence: a unified framework of density-ratio estimation (Q1926013) (← links)
• A co-classification approach to learning from multilingual corpora (Q1959569) (← links)
• On the equivalence of weak learnability and linear separability: new relaxations and efficient boosting algorithms (Q1959594) (← links)
• Mining adversarial patterns via regularized loss minimization (Q1959609) (← links)
• On the convergence of a block-coordinate incremental gradient method (Q2100401) (← links)
• A new accelerated proximal boosting machine with convergence rate \(O(1/t^2)\) (Q2103099) (← links)
• A secant-based Nesterov method for convex functions (Q2361131) (← links)
• Early stopping in \(L_{2}\)Boosting (Q2445675) (← links)
• Texture and shape information fusion for facial expression and facial action unit recognition (Q2462575) (← links)
• Analysis of boosting algorithms using the smooth margin function (Q2473080) (← links)
• Boosting with early stopping: convergence and consistency (Q2583412) (← links)
• Fingerprint classification based on Adaboost learning from singularity features (Q2654288) (← links)
• Robust Algorithms via PAC-Bayes and Laplace Distributions (Q2805741) (← links)
• Approximate Bregman near neighbors in sublinear time: beyond the triangle inequality (Q2875644) (← links)
• Component-wise AdaBoost algorithms for high-dimensional binary classification and class probability prediction (Q3295738) (← links)
• Theory of Classification: a Survey of Some Recent Advances (Q3373749) (← links)
• Automated trading with boosting and expert weighting (Q3564810) (← links)
• Re-examination of Bregman functions and new properties of their divergences (Q4613995) (← links)
• Can a corporate network and news sentiment improve portfolio optimization using the Black–Litterman model? (Q4619505) (← links)
• Information Geometry of U-Boost and Bregman Divergence (Q4832497) (← links)
• Boosting in the Presence of Outliers: Adaptive Classification With Nonconvex Loss Functions (Q4962433) (← links)
• Some Universal Insights on Divergences for Statistics, Machine Learning and Artificial Intelligence (Q4967759) (← links)
• Randomized Gradient Boosting Machine (Q4971024) (← links)
• Extended Newton Methods for Multiobjective Optimization: Majorizing Function Technique and Convergence Analysis (Q5234284) (← links)
• Incremental Majorization-Minimization Optimization with Application to Large-Scale Machine Learning (Q5254990) (← links)
• A boosting inspired personalized threshold method for sepsis screening (Q5861518) (← links)
• The synergy between PAV and AdaBoost (Q5896776) (← links)
• The synergy between PAV and AdaBoost (Q5920546) (← links)
• Conformal mirror descent with logarithmic divergences (Q6138802) (← links)
• An algorithm for learning representations of models with scarce data (Q6660916) (← links)