Learning Updatable Classifiers from Remote Data
نویسندگان
چکیده
The increasing availability of large data offers an exciting opportunity to use such data to build predictive models using machine learning algorithms. However, most approaches to learning assume direct access to data, and can not efficiently cope with frequent updates to the data. In this paper we show that learning using statistical queries provides a powerful paradigm to address these challenges. We summarize our work and present INDUS, an open source implementation of learning algorithms based on the proposed statistical query paradigm.
منابع مشابه
Support Vector Machines for Remote-Sensing Image Classification
In the last decade, the application of statistical and neural network classifiers to remote-sensing images has been deeply investigated. Therefore, performances, characteristics, and pros and cons of such classifiers are quite well known, even from remote-sensing practitioners. In this paper, we present the application to remote-sensing image classification of a new pattern recognition techniqu...
متن کاملEnhancing Learning from Imbalanced Classes via Data Preprocessing: A Data-Driven Application in Metabolomics Data Mining
This paper presents a data mining application in metabolomics. It aims at building an enhanced machine learning classifier that can be used for diagnosing cachexia syndrome and identifying its involved biomarkers. To achieve this goal, a data-driven analysis is carried out using a public dataset consisting of 1H-NMR metabolite profile. This dataset suffers from the problem of imbalanced classes...
متن کاملExamining the Capability of Supervised Machine Learning Classifiers in Extracting Flooded Areas from Landsat TM Imagery: A Case Study from a Mediterranean Flood
This study explored the capability of Support Vector Machines (SVMs) and regularised kernel Fisher’s discriminant analysis (rkFDA) machine learning supervised classifiers in extracting flooded area from optical Landsat TM imagery. The ability of both techniques was evaluated using a case study of a riverine flood event in 2010 in a heterogeneous Mediterranean region, for which TM imagery acquir...
متن کاملUsing intelligent software agents to dynamically build electronic catalogs from E-Commerce web sites
With the increasing number of e-commerce sites, looking for and gathering information is becoming more and more tedious, up to the point where the volume of available data may discourage the customers from browsing the Web to shop. We need to automate this step of product brokering, which is one of the first steps in the consumer buying behaviour model [1, 2]. To do so, we use machine learning ...
متن کاملEvaluation of remote sensing indicators in drought monitoring using machine learning algorithms (Case study: Marivan city)
Remote sensing indices are used to analyze the Spatio-temporal distribution of drought conditions and to identify the severity of drought. This study, using various drought indices generated from Madis and TRMM satellite data extracted from Google Earth Engine (GEE) platform. Drought conditions in Marivan city from February to November for the years 2001 to 2017 were analyzed based on spatial a...
متن کامل