Metric learning for sequences in relational LVQ

نویسندگان
چکیده

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Metric learning for sequences in relational LVQ

Metric learning constitutes a well-investigated field for vectorial data with successful applications, e.g. in computer vision, information retrieval, or bioinformatics. One particularly promising approach is offered by lowrank metric adaptation integrated into modern variants of learning vector quantization (LVQ). This technique is scalable with respect to both, data dimensionality and the num...

متن کامل

Learning a Metric for Relational Data

The vast majority of metric learning approaches are dedicated to be applied on data described by feature vectors, with some notable exceptions such as times series and trees or graphs. The objective of this paper is to propose metric learning algorithms that consider (multi)-relational data. The proposed approach takes benefit from both the topological structure of the data and supervised labels.

متن کامل

Learning Relational Grammars from Sequences of Actions

Many tasks can be described by sequences of actions that normally exhibit some form of structure and that can be represented by a grammar. This paper introduces FOSeq, an algorithm that learns grammars from sequences of actions. The sequences are given as low-level traces of readings from sensors that are transformed into a relational representation. Given a transformed sequence, FOSeq identifi...

متن کامل

Margin based Active Learning for LVQ Networks

In this article, we extend a local prototype-based learning model by active learning, which gives the learner the capability to select training samples and thereby increase speed and accuracy of the model. Our algorithm is based on the idea of selecting a query on the borderline of the actual classification. This can be done by considering margins in an extension of learning vector quantization...

متن کامل

Hyperparameter Learning in Robust Soft LVQ

We present a technique to extend Robust Soft Learning Vector Quantization (RSLVQ). This algorithm is derived from an explicit cost function and follows the dynamics of a stochastic gradient ascent. The RSLVQ cost function involves a hyperparameter which is kept fixed during training. We propose to adapt the hyperparameter based on the gradient information. Experiments on artificial and real lif...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: Neurocomputing

سال: 2015

ISSN: 0925-2312

DOI: 10.1016/j.neucom.2014.11.082