Online Learning for Structural Kernels
نویسندگان
چکیده
While online learning techniques have existed since Rosenblatt’s introduction of the Perceptron in 1957, there has been a renewed interest lately due to the need for efficient classification algorithms. Additionally, kernel techniques have allowed online learning to be extended to problems whose classes are not linearly separable in their native space. Online algorithms typically result in poorer performance than support vector machines, but they have the advantage of vastly reduced computational resources and in some cases do offer performance comparable to the state of the art. This work provides an implementation of several state of the art online learning algorithms as part of the SVM-Light-TK software package, and provides several evaluations of the same. Novelties include the evaluation of such algorithms in the face of high-dimensional structural kernels, and their application to Question Classification and Semantic Role Labelling Boundary Classification tasks from the NLP domain. Additionally, we present a comparison of these online algorithms with batch mode Support Vector Machines.
منابع مشابه
Online Learning with Multiple Operator-valued Kernels
We consider the problem of learning a vector-valued function f in an online learning setting. The function f is assumed to lie in a reproducing Hilbert space of operator-valued kernels. We describe two online algorithms for learning f while taking into account the output structure. A first contribution is an algorithm, ONORMA, that extends the standard kernel-based online learning algorithm NOR...
متن کاملLarge-Scale Learning with Structural Kernels for Class-Imbalanced Datasets
Much of the success in machine learning can be attributed to the ability of learning methods to adequately represent, extract, and exploit inherent structure present in the data under interest. Kernel methods represent a rich family of techniques that harvest on this principle. Domain-specific kernels are able to exploit rich structural information present in the input data to deliver state of ...
متن کاملOnline Learning Algorithm for Structural Control Using Magnetorheological Actuators
Magnetorheological actuators are promising devices for mitigating vibrations because they only require a fraction of energy for a similar performance to active control. Conversely, these semi-active devices have limited maximum forces and are hard to model due to the rheological properties of their fluid. When considering structural control, classical theories necessitate full knowledge of the ...
متن کاملOnline Pairwise Learning Algorithms with Kernels
Pairwise learning usually refers to a learning task which involves a loss function depending on pairs of examples, among which most notable ones include ranking, metric learning and AUC maximization. In this paper, we study an online algorithm for pairwise learning with a least-square loss function in an unconstrained setting of a reproducing kernel Hilbert space (RKHS), which we refer to as th...
متن کاملOnline Learning with (Multiple) Kernels: A Review
This review examines kernel methods for online learning, in particular, multiclass classification. We examine margin-based approaches, stemming from Rosenblatt's original perceptron algorithm, as well as nonparametric probabilistic approaches that are based on the popular gaussian process framework. We also examine approaches to online learning that use combinations of kernels--online multiple ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2011