Improving boosting methods with a stable loss function handling outliers

Boosting in the presence of outliers: adaptive classification with non-convex loss functions

This paper examines the role and efficiency of the non-convex loss functions for binary classification problems. In particular, we investigate how to design a simple and effective boosting algorithm that is robust to the outliers in the data. The analysis of the role of a particular non-convex loss for prediction accuracy varies depending on the diminishing tail properties of the gradient of th...

The Most Robust Loss Function for Boosting

Boosting algorithm is understood as the gradient descent algorithm of a loss function. It is often pointed out that the typical boosting algorithm, Adaboost, is seriously affected by the outliers. In this paper, loss functions for robust boosting are studied. Based on a concept of the robust statistics, we propose a positive-part-truncation of the loss function which makes the boosting algorith...

Boosting with log-loss

Improving Tree Decomposition Methods With Function Filtering

Iterative MCTEf IMCTE φ(u, v) = M (v, u) the approximated MCTEf message of a previous execution (one with minor r) procedure IMCTEf(〈X,D,C, k〉, 〈〈V,E〉, χ, ψ〉) INPUT: P = 〈X,D,C, k〉 is a WCSP instance TD = 〈〈V,E〉, χ, ψ〉 is a tree decomposition. 1 for each (u, v) ∈ E do φ(u, v) := {∅}; 2 r := 1; 3 repeat 4 MCTEf(r); 5 for each (u, v) ∈ E do φ(u, v) :=M(u,v); 6 r := r + 1; 7 until exact solution o...

Forecasting the Tehran Stock market by Machine ‎Learning Methods using a New Loss Function

Stock market forecasting has attracted so many researchers and investors that ‎many studies have been done in this field. These studies have led to the ‎development of many predictive methods, the most widely used of which are ‎machine learning-based methods. In machine learning-based methods, loss ‎function has a key role in determining the model weights. In this study a new loss ‎function is ...