A life-long learning vector quantization approach for interactive learning of multiple categories
TLDR
To achieve the life-long learning ability for a cognitive system, a new learning vector quantization approach combined with a category-specific feature selection method to allow several metrical "views" on the representation space of each individual vector quantification node.About:
This article is published in Neural Networks.The article was published on 2012-04-01 and is currently open access. It has received 53 citations till now. The article focuses on the topics: Semi-supervised learning & Active learning (machine learning).read more
Citations
More filters
Journal ArticleDOI
A Novel Semisupervised Deep Learning Method for Human Activity Recognition
TL;DR: This paper proposes a semisupervised deep learning approach, using temporal ensembling of deep long short-term memory, to recognize human activities with smartphone inertial sensors, and proposes an ensemble approach based on both labeled and unlabeled data, which can combine together the supervised and unsupervised losses.
Journal ArticleDOI
Towards lifelong assistive robotics: A tight coupling between object perception and manipulation
TL;DR: Experimental results show that the proposed system is able to interact with human users, learn new object categories over time, as well as perform complex tasks.
Journal ArticleDOI
3D object perception and perceptual learning in the RACE project
Miguel Oliveira,Luís Seabra Lopes,Gi Hyun Lim,S. Hamidreza Kasaei,Ana Maria Tomé,Aneesh Chauhan +5 more
TL;DR: An object perception and perceptual learning system developed for a complex artificial cognitive agent working in a restaurant scenario that integrates detection, tracking, learning and recognition of tabletop objects and the Point Cloud Library is used in nearly all modules.
Journal ArticleDOI
Interactive Open-Ended Learning for 3D Object Recognition: An Approach and Experiments
TL;DR: An efficient approach capable of learning and recognizing object categories in an interactive and open-ended manner, which is able to interact with human users, learning new object categories continuously over time is presented.
Journal ArticleDOI
Efficient rejection strategies for prototype-based classification
TL;DR: This contribution presents simple and efficient reject options for prototype-based classification, and it is demonstrated that the proposed reject options improve the accuracy in most cases and their performance is comparable to an optimal reject option of the Bayes classifier in cases where the latter is available.
References
More filters
Journal ArticleDOI
Distinctive Image Features from Scale-Invariant Keypoints
TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.
Journal ArticleDOI
Support-Vector Networks
Corinna Cortes,Vladimir Vapnik +1 more
TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
Book
Pattern Recognition and Machine Learning
TL;DR: Probability Distributions, linear models for Regression, Linear Models for Classification, Neural Networks, Graphical Models, Mixture Models and EM, Sampling Methods, Continuous Latent Variables, Sequential Data are studied.
Journal ArticleDOI
Pattern Recognition and Machine Learning
TL;DR: This book covers a broad range of topics for regular factorial designs and presents all of the material in very mathematical fashion and will surely become an invaluable resource for researchers and graduate students doing research in the design of factorial experiments.
Proceedings ArticleDOI
Rapid object detection using a boosted cascade of simple features
Paul A. Viola,Michael Jones +1 more
TL;DR: A machine learning approach for visual object detection which is capable of processing images extremely rapidly and achieving high detection rates and the introduction of a new image representation called the "integral image" which allows the features used by the detector to be computed very quickly.