Passive-Aggressive online learning with nonlinear embeddings
Nowadays, there is an increasing demand for machine learning techniques which can deal with problems where the instances are produced as a stream or in real time. In these scenarios, online learning is able to learn a model from data that comes continuously. The adaptability, efficiency and scalability of online learning techniques have been gaining interest last years with the increasing amount of data generated every day. In this paper, we propose a novel binary classification approach based on nonlinear mapping functions under an online learning framework. The non-convex optimization problem that arises is split into three different convex problems that are solved by means of Passive-Aggressive Online Learning. We evaluate both the adaptability and generalization of our model through several experiments comparing with the state of the art techniques. We improve significantly the results in several datasets widely used previously by the online learning community.
Code (1)
Tasks
Binary ClassificationSimilar Papers 제목 키워드 기반
An online passive-aggressive algorithm for difference-of-squares classification
We investigate a low-rank model of quadratic classification inspired by previous work on factorization machines, polynomial networks, and capsule-based architectures for visual object recognition. The model is parameteri…
ClassificationObject RecognitionAn online passive-aggressive algorithm for difference-of-squares classification
We investigate a low-rank model of quadratic classification inspired by previous work on factorization machines, polynomial networks, and capsule-based architectures for visual object recognition. The model is parameteri…
ClassificationObject RecognitionOnline Bayesian Passive-Aggressive Learning
Online Passive-Aggressive (PA) learning is an effective framework for performing max-margin online learning. But the deterministic formulation and estimated single large-margin model could limit its capability in discove…
Bayesian InferenceDescriptiveTopic ModelsConfusion-Based Online Learning and a Passive-Aggressive Scheme
This paper provides the first ---to the best of our knowledge--- analysis of online learning algorithms for multiclass problems when the {\em confusion} matrix is taken as a performance measure. The work builds upon rece…
Generalization BoundsOnline Passive-Aggressive Total-Error-Rate Minimization
We provide a new online learning algorithm which utilizes online passive-aggressive learning (PA) and total-error-rate minimization (TER) for binary classification. The PA learning establishes not only large margin train…
Binary ClassificationGeneral Classification