Generally this approach is called functional gradient descent or gradient descent with functions. One way to produce a weighted combination of classifiers which optimizes [the cost] is by gradient descent in function space —Boosting Algorithms as Gradient Descent in Function Space[PDF], 1999 The ou...
instead of setting a weight for every example, gradient boosting builds each new weak learner on the residuals of the previous linear combination. We can see gradient boosting as gradient descent in functional space. The linear combination
Grafting: Fast, Incremental Feature Selection by Gradient Descent in Function Space We present a novel and flexible approach to the problem of feature selection, called S Perkins,K Lacker,J Theiler - 《Journal of Machine Learning Research》 被引量: 489发表: 2003年 Learning to Learn Using Gradie...
Gradient descent generalises naturally to Riemannian manifolds, and to hyperbolic n n -space, in particular. Namely, having calculated the gradient at the point on the manifold representing the model parameters, the updated point is obtained by travelling along the geodesic passing in the direction ...
The next, I guess, time period of your research that you tend to focus on is uncovering the fundamental difficulty of learning in recurrent nets. And I thought that the "Learning Long-Term Dependencies with Gradient Descent is Difficult" was a really interesting paper. I thought it was kind...
Gradient descent is, in fact, a general-purpose optimization technique that can be applied whenever the objective function is differentiable. Actually, it turns out that it can even be applied in cases where the objective function is not completely differentiable through use of a device called ...
is not “direct” as in Gradient Descent, but may go “zig-zag” if we are visuallizing the cost surface in a 2D space. However, it has been shown that Stochastic Gradient Descent almost surely converges to the global cost minimum if the cost function is convex (or pseudo-convex)[1]...
In the current contribution we do not answer this questions, but rather present the mathematical framework within which the evolution of discrete disloca-tions is literally understood as a gradient descent. The suggested framework is that of de Rham currents and differential forms. We briefly sketch...
However, I was confused about gradient everytime when I start to think about the details in graident descent. Why the gradient is the steepest direction of the function space? Why not other directional vectors? Now, I figure it out. As we all know that every space in the university can ...
We characterize the limit points of two basic first order methods, namely Gradient Descent/Ascent (GDA) and Optimistic Gradient Descent Ascent (OGDA). We show that both dynamics avoid unstable critical points for almost all initializations. Moreover, for small step sizes and under mild assumptions...