H.3.7. Learning
Laleh Armi; Elham Abbasi
Abstract
In this paper, we propose an innovative classification method for tree bark classification and tree species identification. The proposed method consists of two steps. In the first step, we take the advantages of ILQP, a rotationally invariant, noise-resistant, and fully descriptive color texture feature ...
Read More
In this paper, we propose an innovative classification method for tree bark classification and tree species identification. The proposed method consists of two steps. In the first step, we take the advantages of ILQP, a rotationally invariant, noise-resistant, and fully descriptive color texture feature extraction method. Then, in the second step, a new classification method called stacked mixture of ELM-based experts with a trainable gating network (stacked MEETG) is proposed. The proposed method is evaluated using the Trunk12, BarkTex, and AFF datasets. The performance of the proposed method on these three bark datasets shows that our approach provides better accuracy than other state-of-the-art methods.Our proposed method achieves an average classification accuracy of 92.79% (Trunk12), 92.54% (BarkTex), and 91.68% (AFF), respectively. Additionally, the results demonstrate that ILQP has better texture feature extraction capabilities than similar methods such as ILTP. Furthermore, stacked MEETG has shown a great influence on the classification accuracy.
H.3.7. Learning
M. Farhid; M. Shamsi; M. H. Sedaaghi
Abstract
Adaptive networks include a set of nodes with adaptation and learning abilities for modeling various types of self-organized and complex activities encountered in the real world. This paper presents the effect of heterogeneously distributed incremental LMS algorithm with ideal links on the quality of ...
Read More
Adaptive networks include a set of nodes with adaptation and learning abilities for modeling various types of self-organized and complex activities encountered in the real world. This paper presents the effect of heterogeneously distributed incremental LMS algorithm with ideal links on the quality of unknown parameter estimation. In heterogeneous adaptive networks, a fraction of the nodes, defined based on previously calculated signal to noise ratio (SNR), is assumed to be the informed nodes that collect data and perform in-network processing, while the remaining nodes are assumed to be uninformed and only participate in the processing tasks. As our simulation results show, the proposed algorithm not only considerably improves the performance of the Distributed Incremental LMS algorithm in a same condition, but also proves a good accuracy of estimation in cases where some of the nodes make unreliable observations (noisy nodes). Also studied is the application of the same algorithm on the cases where node failure happens
