289,836 research outputs found
Learning from the past with experiment databases
Thousands of Machine Learning research papers contain experimental comparisons that usually have been conducted with a single focus of interest, and detailed results are usually lost after publication. Once past experiments are collected in experiment databases they allow for additional and possibly much broader investigation. In this paper, we show how to use such a repository to answer various interesting research questions about learning algorithms and to verify a number of recent studies. Alongside performing elaborate comparisons and rankings of algorithms, we also investigate the effects of algorithm parameters and data properties, and study the learning curves and bias-variance profiles of algorithms to gain deeper insights into their behavior
Experiment Databases: Creating a New Platform for Meta-Learning Research
Many studies in machine learning try to investigate what makes an algorithm succeed or fail on certain datasets. However, the field is still evolving relatively quickly, and new algorithms, preprocessing methods, learning tasks and evaluation procedures continue to emerge in the literature. Thus, it is impossible for a single study to cover this expanding space of learning approaches. In this paper, we propose a community-based approach for the analysis of learning algorithms, driven by sharing meta-data from previous experiments in a uniform way. We illustrate how organizing this information in a central database can create a practical public platform for any kind of exploitation of meta-knowledge, allowing effective reuse of previous experimentation and targeted analysis of the collected results
Enabling On-Demand Database Computing with MIT SuperCloud Database Management System
The MIT SuperCloud database management system allows for rapid creation and
flexible execution of a variety of the latest scientific databases, including
Apache Accumulo and SciDB. It is designed to permit these databases to run on a
High Performance Computing Cluster (HPCC) platform as seamlessly as any other
HPCC job. It ensures the seamless migration of the databases to the resources
assigned by the HPCC scheduler and centralized storage of the database files
when not running. It also permits snapshotting of databases to allow
researchers to experiment and push the limits of the technology without
concerns for data or productivity loss if the database becomes unstable.Comment: 6 pages; accepted to IEEE High Performance Extreme Computing (HPEC)
conference 2015. arXiv admin note: text overlap with arXiv:1406.492
First experience in operating the population of the condition databases for the CMS experiment
Reliable population of the condition databases is critical for the correct
operation of the online selection as well as of the offline reconstruction and
analysis of data. We will describe here the system put in place in the CMS
experiment to populate the database and make condition data promptly available
both online for the high-level trigger and offline for reconstruction. The
system, designed for high flexibility to cope with very different data sources,
uses POOL-ORA technology in order to store data in an object format that best
matches the object oriented paradigm for \texttt{C++} programming language used
in the CMS offline software. In order to ensure consistency among the various
subdetectors, a dedicated package, PopCon (Populator of Condition Objects), is
used to store data online. The data are then automatically streamed to the
offline database hence immediately accessible offline worldwide. This mechanism
was intensively used during 2008 in the test-runs with cosmic rays. The
experience of this first months of operation will be discussed in detail.Comment: 15 pages, submitter to JOP, CHEP0
Interlingual Lexical Organisation for Multilingual Lexical Databases in NADIA
We propose a lexical organisation for multilingual lexical databases (MLDB).
This organisation is based on acceptions (word-senses). We detail this lexical
organisation and show a mock-up built to experiment with it. We also present
our current work in defining and prototyping a specialised system for the
management of acception-based MLDB. Keywords: multilingual lexical database,
acception, linguistic structure.Comment: 5 pages, Macintosh Postscript, published in COLING-94, pp. 278-28
An evaluation of a three-modal hand-based database to forensic-based gender recognition
In recent years, behavioural soft-biometrics have been widely used to
improve biometric systems performance. Information like gender, age and ethnicity can be obtained from more than one behavioural modality. In this paper,
we propose a multimodal hand-based behavioural database for gender recognition. Thus, our goal in this paper is to evaluate the performance of the multimodal database. For this, the experiment was realised with 76 users and was
collected keyboard dynamics, touchscreen dynamics and handwritten signature
data. Our approach consists of compare two-modal and one-modal modalities
of the biometric data with the multimodal database. Traditional and new classifiers were used and the statistical Kruskal-Wallis to analyse the accuracy of the
databases. The results showed that the multimodal database outperforms the
other databases
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