Rosen's projection method for SVM training
نویسندگان
چکیده
In this work we will give explicit formulae for the application of Rosen’s gradient projection method to SVM training that leads to a very simple implementation. We shall experimentally show that the method provides good descent directions that result in less training iterations, particularly when large precision is wanted. However, a naive kernelization may end up in a procedure requiring more KOs than SMO and further work is needed to arrive at an efficient implementation.
منابع مشابه
Semi-Supervised Learning for Relation Extraction
This paper proposes a semi-supervised learning method for relation extraction. Given a small amount of labeled data and a large amount of unlabeled data, it first bootstraps a moderate number of weighted support vectors via SVM through a co-training procedure with random feature projection and then applies a label propagation (LP) algorithm via the bootstrapped support vectors. Evaluation on th...
متن کاملDiscriminant NAP for SVM speaker recognition
Nuisance Attribute Projection (NAP) provides an effective method of removing the unwanted session variability in a Support Vector Machine (SVM) based speaker recognition system by removing the principal components of this variability. There is no guarantee with the methods proposed, however, that desired speaker variability is retained. This paper investigates the possibility of training NAP di...
متن کاملECT and LS-SVM Based Void Fraction Measurement of Oil-Gas Two-Phase Flow
A method based on Electrical Capacitance Tomography (ECT) and an improved Least Squares Support Vector Machine (LS-SVM) is proposed for void fraction measurement of oil-gas two-phase flow. In the modeling stage, to solve the two problems in LS-SVM, pruning skills are employed to make LS-SVM sparse and robust; then the Real-Coded Genetic Algorithm is introduced to solve the difficult problem...
متن کاملRandom Projection Features and Generalized Additive Models
We propose to learn generalized additive models for classification which represents the classifier using a sum of piecewise linear functions and show that a recently proposed fast linear SVM training method (Pegasos) can be adapted to train such models with the same convergence rates. To be able to learn functions on combination of dimensions, we explore the use of random projection features wh...
متن کاملLabel propagation via bootstrapped support vectors for semantic relation extraction between named entities
This paper proposes a semi-supervised learning method for semantic relation extraction between named entities. Given a small amount of labeled data, it benefits much from a large amount of unlabeled data by first bootstrapping a moderate number of weighted support vectors from all the available data through a co-training procedure on top of support vector machines (SVM) with feature projection ...
متن کامل