Using Active Learning to Allow Activity Recognition on a Large Scale

Date

2011-11-18

Author

Alemdar, Hande
ERSOY, CEM

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

171
views

0
downloads

Automated activity recognition systems that use probabilistic models require labeled data sets in training phase for learning the model parameters. The parameters are different for every person and every environment. Therefore, for every person or environment, training is needed to be performed from scratch. Obtaining labeled data requires much effort therefore poses challenges on the large scale deployment of activity recognition systems. Active learning can be a solution to this problem. It is a machine learning technique that allows the algorithm to choose the most informative data points to be annotated. Because the algorithm selects the most informative data points, the amount of the labeled data needed for training the model is reduced. In this study, we propose using active learning methods for activity recognition. We use three different informativeness measures for selecting the most informative data points and evaluate their performances using three real world data sets recorded in a home setting. We show through experiments that the required number of data points is reduced by 80% in House A, 73% in House B, and 66% in House C with active learning.

Subject Keywords

Ambient

URI

https://hdl.handle.net/11511/48498

DOI

https://doi.org/10.1007/978-3-642-25167-2_12

Collections

Department of Computer Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

Learning to rank web data using multivariate adaptive regression splines Altınok, Gülşah; Batmaz, İnci; Karagöz, Pınar; Department of Statistics (2018) A new trend, called learning to rank, has recently come to light in a wide variety of applications in Information Retrieval (IR), Natural Language Processing (NLP), and Data Mining (DM), to utilize machine learning techniques to automatically build the ranking models. Typical applications are document retrieval, expert search, definition search, collaborative filtering, question answering, and machine translation. In IR, there are three approaches used for ranking. The one is traditional model approaches su...
Using data analytics for collaboration patterns in distributed software team simulations Dafoulas, Georgios A.; Serce, Fatma C.; SWİGGER, Kathleen; BRAZİLE, Robert; Alpaslan, Ferda Nur; Alpaslan, Ferda Nur; Milewski, Allen (2016-08-05) This paper discusses how previous work on global software development learning teams is extended with the introduction of data analytics. The work is based on several years of studying student teams working in distributed software team simulations. The scope of this paper is twofold. First it demonstrates how data analytics can be used for the analysis of collaboration between members of distributed software teams. Second it describes the development of a dashboard to be used for the visualization of variou...
A deep learning approach for the transonic flow field predictions around airfoils Duru, Cihat; Alemdar, Hande; Baran, Özgür Uğraş (2022-01-01) Learning from data offers new opportunities for developing computational methods in research fields, such as fluid dynamics, which constantly accumulate a large amount of data. This study presents a deep learning approach for the transonic flow field predictions around airfoils. The physics of transonic flow is integrated into the neural network model by utilizing Reynolds-averaged Navier–Stokes (RANS) simulations. A detailed investigation on the performance of the model is made both qualitatively and quant...
Digital output ROIC with single slope ADC for cooled infrared applications Akyurek, Fatih; Bayram, Barış (2017-04-01) The objective of this research is to develop an ADC stage integrated into ROIC which enables ROIC to have digital output. Digital output method isolates noise caused by outside mediums. At the system level, removal of the ADC proximity card reduces system complexity and volume of the IDDCA system which is important for avionic and missile applications. It also reduces the system cost associated with external ADC components. A digital output ROIC utilizing single slope ADC is fabricated using 0.18 A mu m CMO...
A new likelihood approach to autonomous multiple model estimation Söken, Halil Ersin (Elsevier BV, 2020-04-01) This paper presents an autonomous multiple model (AMM) estimation algorithm for hybrid systems with sudden changes in their parameters. Estimates of Kalman filters (KFs) that are tuned and employed for different system modes are merged based on a newly defined likelihood function without any necessity for filter interaction. The proposed likelihood function is composed of two measures, the filter agility measure and the steady-state error measure. These measures are derived based on filter adaptation rules....

Citation Formats

H. Alemdar and C. ERSOY, “Using Active Learning to Allow Activity Recognition on a Large Scale,” 2011, vol. 7040, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/48498.