A low-complexity activity classification algorithm with optimized selection of accelerometric features

2016 ◽  
Vol 8 (6) ◽  
pp. 681-695
Author(s):  
Matteo Giuberti ◽  
Gianluigi Ferrari
Keyword(s):  
Low Complexity ◽  
Classification Algorithm ◽  
Activity Classification ◽  
Selection Of

scholarly journals Transfer Incremental Learning Using Data Augmentation

2018 ◽  
Vol 8 (12) ◽  
pp. 2512 ◽  
Author(s):  
Ghouthi Boukli Hacene ◽  
Vincent Gripon ◽  
Nicolas Farrugia ◽  
Matthieu Arzel ◽  
Michel Jezequel
Keyword(s):  
Incremental Learning ◽  
Deep Neural Networks ◽  
Data Augmentation ◽  
State Of The Art ◽  
Low Complexity ◽  
Computational Power ◽  
Learning Problem ◽  
Learning Techniques ◽  
Using Data ◽  
Selection Of

Deep learning-based methods have reached state of the art performances, relying on a large quantity of available data and computational power. Such methods still remain highly inappropriate when facing a major open machine learning problem, which consists of learning incrementally new classes and examples over time. Combining the outstanding performances of Deep Neural Networks (DNNs) with the flexibility of incremental learning techniques is a promising venue of research. In this contribution, we introduce Transfer Incremental Learning using Data Augmentation (TILDA). TILDA is based on pre-trained DNNs as feature extractors, robust selection of feature vectors in subspaces using a nearest-class-mean based technique, majority votes and data augmentation at both the training and the prediction stages. Experiments on challenging vision datasets demonstrate the ability of the proposed method for low complexity incremental learning, while achieving significantly better accuracy than existing incremental counterparts.

Naive Bayes Classification Algorithm Based on Optimized Training Data

2012 ◽  
Vol 490-495 ◽  
pp. 460-464 ◽  
Author(s):  
Xiao Dan Zhu ◽  
Jin Song Su ◽  
Qing Feng Wu ◽  
Huai Lin Dong
Keyword(s):  
Large Scale ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Classification Algorithm ◽  
Training Data ◽  
Large Scale Data ◽  
Naive Bayes Classification ◽  
Naïve Bayes Classification ◽  
Effectiveness Threshold ◽  
Selection Of

Naive Bayes classification algorithm is an effective simple classification algorithm. Most researches in traditional Naive Bayes classification focus on the improvement of the classification algorithm, ignoring the selection of training data which has a great effect on the performance of classifier. And so a method is proposed to optimize the selection of training data in this paper. Adopting this method, the noisy instances in training data are eliminated by user-defined effectiveness threshold, improving the performance of classifier. Experimental results on large-scale data show that our approach significantly outperforms the baseline classifier.

scholarly journals Learning to Play Using Low-Complexity Rule-Based Policies: Illustrations through Ms. Pac-Man

2007 ◽  
Vol 30 ◽  
pp. 659-684 ◽  
Author(s):  
I. Szita ◽  
A. Lorincz
Keyword(s):  
Low Complexity ◽  
Entropy Method ◽  
Cross Entropy ◽  
Rule Based ◽  
Learning Tasks ◽  
Large Pool ◽  
Cross Entropy Method ◽  
High Level ◽  
Selection Of ◽  
Better Than

In this article we propose a method that can deal with certain combinatorial reinforcement learning tasks. We demonstrate the approach in the popular Ms. Pac-Man game. We define a set of high-level observation and action modules, from which rule-based policies are constructed automatically. In these policies, actions are temporally extended, and may work concurrently. The policy of the agent is encoded by a compact decision list. The components of the list are selected from a large pool of rules, which can be either hand-crafted or generated automatically. A suitable selection of rules is learnt by the cross-entropy method, a recent global optimization algorithm that fits our framework smoothly. Cross-entropy-optimized policies perform better than our hand-crafted policy, and reach the score of average human players. We argue that learning is successful mainly because (i) policies may apply concurrent actions and thus the policy space is sufficiently rich, (ii) the search is biased towards low-complexity policies and therefore, solutions with a compact description can be found quickly if they exist.

scholarly journals Cooperative Selection and Communications for Internet of Everything in 6G Ultra-mMTC IoT Networks

2021 ◽  
Author(s):  
He Huang ◽  
Su Hu
Keyword(s):  
Cooperative Communications ◽  
Communication Technologies ◽  
Low Complexity ◽  
Underwater Acoustic Communication ◽  
Collaborative Communication ◽  
Integrated Networks ◽  
Internet Of Everything ◽  
Critical Problems ◽  
4G Lte ◽  
Selection Of

<p>Although Cooperative Communications (CC) already exists in 4G LTE/5G, performance improvement is just not enough, hence, how to apply collaborative technology in 6G to achieve Internet of Everything and improve communication quality significantly is one of most important issues. As 5G communications standard has gradually established recently, CC has been one of most critical communication technologies which plays a founding role on Internet of Everything in 6G networks. Thus, in this study we propose new 6G enabled ultra-massive Machine Type Communication (mMTC) framework based on space-ground integrated networks, and consider that collaborative ideology has been regarded as foundation theory which widely exists in future multiple hybrid scenarios, such as, Cognitive-Internet of Things (C-IoT) networks, UAVs (Unmanned Aerial Vehicles) communications, air-space-ground integrated networks, underwater acoustic communication and so on. Besides, we discuss other key communication technologies that are closely combined with unique communication scenes, and elaborate critical problems that shall be solved in 6G ultra-mMTC Internet of Everything networks. Furthermore, we discuss extreme low-complexity and fast ultra-massive relays selection algorithm to apply in all sorts of future 6G scenarios. At last, it is concluded that for arbitrary two communication points (source/destination devices, sensors, relays, IoT nodes and so on), ideology of nodes selection of collaborative communication is theory of foundation to realize and optimize communication transmission in 6G Internet of Everything ultra-mMTC networks. </p>

BSN-Based Activity Classification: A Low Complexity Windowing-&-Classification Approach

2012 ◽  
Vol 85 ◽  
pp. 53-58 ◽  
Author(s):  
Matteo Giuberti ◽  
Gianluigi Ferrari
Keyword(s):  
Sensor Networks ◽  
Low Complexity ◽  
The Body ◽  
Body Sensor Networks ◽  
Detection Accuracy ◽  
Activity Classification ◽  
First Case ◽  
Monitoring Applications ◽  
Healthcare Monitoring ◽  
The Time Domain

Wireless sensor networks (WSNs) are becoming more and more attractive because of their flexibility. In particular, WSNs are being applied to a user body in order to monitor and detect some activities of daily living (ADL) performed by the user (e.g., for medical purposes). This class of WSNs are typically denoted as body sensor networks (BSNs). In this paper, we discuss BSN-based human activity classification. In particular, the goal of our approach is to detect a sequence of activities, chosen from a limited set of fixed known activities, by observing the outputs generated by accelerometers and gyroscopes at the sensors placed over the body. In general, our framework is based on low-complexity windowing-&-classification. First, we consider the case of disjoint (in the time domain) activities; then, we extend our approach to encompass a scenario with consecutive non-disjoint activities. While in the first case windowing is separate from classification, in the second case windowing and classification need to be carried out jointly. The obtained results show a significant detection accuracy of the proposed method, making it suitable for healthcare monitoring applications.

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