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INCREMENTAL LEARNING IN BIOLOGICAL AND MACHINE LEARNING SYSTEMS

STEPHAN K. CHALUP
2002 International Journal of Neural Systems  
A discussion of experimental results on data incremental learning with recurrent artificial neural networks reveals that incremental learning often seems to be more efficient or powerful than standard  ...  Incremental learning concepts are reviewed in machine learning and neurobiology. They are identified in evolution, neurodevelopment and learning.  ...  This project was supported by the University of Newcastle RMC ECR grant Machines Learn via Biologically Motivated Incremental Algorithms.  ... 
doi:10.1142/s0129065702001308 pmid:12528196 fatcat:di7pjywjqfgzzfpp74cnhebg7u

An introduction to synchronous self-learning Pareto strategy [article]

Ahmad Mozaffari, Alireza Fathi
2013 arXiv   pre-print
Classical Self Organizing Map (CSOM) simultaneously incorporating with a data shuffling behavior.  ...  In this paper a novel method called Synchronous Self Learning Pareto Strategy Algorithm (SSLPSA) is presented which utilizes Evolutionary Computing (EC), Swarm Intelligence (SI) techniques and adaptive  ...  In the following subparts the architecture of classic self organizing map with adaptive gradient learning rule and conscience mechanism will be summarized briefly. 1) Adaptive Self Organizing Map Adaptive  ... 
arXiv:1312.4132v1 fatcat:5q7wxjb4zfdgdoytqh5kihw23u

Player modeling using self-organization in Tomb Raider: Underworld

Anders Drachen, Alessandro Canossa, Georgios N. Yannakakis
2009 2009 IEEE Symposium on Computational Intelligence and Games  
Emergent self-organizing maps are trained on high-level playing behavior data obtained from 1365 players that completed the TRU game.  ...  The unsupervised learning approach utilized reveals four types of players which are analyzed within the context of the game.  ...  The authors would like to thank their colleagues at Crystal Dynamics and IO Interactive (IOI) for continued assistance with access to the EIDOS Metrics Suite and discussion of approaches, methods and results  ... 
doi:10.1109/cig.2009.5286500 dblp:conf/cig/DrachenCY09 fatcat:z6gvxk4g4rcw7fuy7ax3ze3dxu

Task-dependent evolution of modularity in neural networks

Michael Hüsken, Christian Igel, Marc Toussaint
2002 Connection science  
The demand for fast learning systems increases the selective pressure towards modularity.  ...  For this purpose, we define quantitative measures for the degree of modularity and monitor them during evolutionary processes under different constraints.  ...  Summarising, the evolutionary pressure towards architecture-modularity is increased by the task of fast learning.  ... 
doi:10.1080/09540090208559328 fatcat:btdbocp5bvghlk3lsh2xoy3dgq

A deep biometric recognition and diagnosis network with residual learning for arrhythmia screening using electrocardiogram recordings

Hao Dang, Yaru Yue, Danqun Xiong, Xiaoguang Zhou, Xiangdong Xu, Xingxiang Tao
2020 IEEE Access  
Specifically, compared with the self-organizing structural size method [63] [64] [65] , the deep convolutional neural network is complicated to fast determine its optimal structure given specific applications  ...  Hence, we will propose a new method combined the self-organizing maps and convolutional neural network to the ECG signal research in the future work.  ... 
doi:10.1109/access.2020.3016938 fatcat:jkuqswdswzg7tc7lh5o76hu7lu

Boosted Training of Convolutional Neural Networks for Multi-Class Segmentation [article]

Lorenz Berger, Eoin Hyde, Matt Gibb, Nevil Pavithran, Garin Kelly, Faiz Mumtaz, Sébastien Ourselin
2018 arXiv   pre-print
This results in a significant training speed up and improves learning performance for image segmentation. 2) We propose a novel algorithm for boosting the SGD learning rate schedule by adaptively increasing  ...  Our contribution is threefold: 1) We propose a boosted sampling scheme that uses a-posterior error maps, generated throughout training, to focus sampling on difficult regions, resulting in a more informative  ...  Curriculum learning [4] and derivative methods like self-paced learning [5] build on the intuition that, rather than considering all samples simultaneously, the algorithm should be presented with the  ... 
arXiv:1806.05974v2 fatcat:2scfv4up7zfi5ks7qbitbzi6by

Dynamic self-organization in holonic multi-agent manufacturing systems: The ADACOR evolution

José Barbosa, Paulo Leitão, Emmanuel Adam, Damien Trentesaux
2015 Computers in industry (Print)  
In particular, a two-dimensional self-organization mechanism was designed taking the behavioural and structural vectors into consideration, thus allowing truly evolutionary and reconfigurable systems to  ...  According to these principals, several approaches have been proposed but none can be truly embedded and extract all the potential of self-organization mechanisms.  ...  This module addresses two types of self-organization components and comprises primarily the monitoring, discovery, reasoning, learning, nervousness stabilizer and dispatcher components.  ... 
doi:10.1016/j.compind.2014.10.011 fatcat:c2uwn5qe25d7fhdpoxhhh3t7di

Behavioural Validation of the ADACOR2 Self-organized Holonic Multi-agent Manufacturing System [chapter]

José Barbosa, Paulo Leitão, Emmanuel Adam, Damien Trentesaux
2015 Lecture Notes in Computer Science  
Despite of the effort spent, there is still the need to empower those architectures with evolutionary capabilities and self-organization mechanisms to enable the constant adaption to disturbances.  ...  This validation is achieved through the use of a benchmark and results are compared with classical hierarchical and heterarchical architectures as also with the ADACOR.  ...  The first, extends the PROSA reference architecture with inspiration from the ants food foraging that is used as forecast technique, while on the second one, a buffer type self-organization mechanism is  ... 
doi:10.1007/978-3-319-22867-9_6 fatcat:hgvdc4jwv5bblkl7nssxs4w3cu

Behavior-based Systems in Data Science [chapter]

Michael Christoph Thrun
2018 Projection-Based Clustering through Self-Organization and Swarm Intelligence  
By analyzing ant-based clustering 39 (ABC) [Lumer/Faieta, 1994] and the batch self-organizing map (batch-SOM) method [Kohonen/Somervuo, 2002] the local stress of an ABC projection 40 can be extracted  ...  Figure 7 . 4 : 74 Types of swarm algorithms used in unsupervised learning. Pswarm will be introduced in the next chapter; it combines self-organization with swarm intelligence.  ...  In a game with n players, let the k choices of player be defined by a set Π , … , … , , where indicates the player's choice; then, a mixed strategy ∈ for player is defined by where ∑ 1 and all 0.  ... 
doi:10.1007/978-3-658-20540-9_7 fatcat:rntspvbfwnctpfgf7iz7spmegu

Time-Series Trend of Pandemic SARS-CoV-2 Variants Visualized Using Batch-Learning Self-Organizing Map for Oligonucleotide Compositions

Takashi Abe, Ryuki Furukawa, Yuki Iwasaki, Toshimichi Ikemura
2021 Data Science Journal  
Here, we collectively analyzed over 150,000 SARS-CoV-2 genomes to understand their overall features and time-dependent changes using a batch-learning self-organizing map (BLSOM) for oligonucleotide composition  ...  , which is an unsupervised machine learning method.  ...  self-organizing map; BLSOM), which enables separation (self-organization) of the genomic sequences by species and phylogeny and explains the causes that contribute to this separation (Abe et al. 2003  ... 
doi:10.5334/dsj-2021-029 fatcat:2ghc25rccbbddmb6wyn7ad5twe

Unsupervised explainable AI for molecular evolutionary study of forty thousand SARS-CoV-2 genomes

Yuki Iwasaki, Takashi Abe, Kennosuke Wada, Yoshiko Wada, Toshimichi Ikemura
2022 BMC Microbiology  
We previously established unsupervised AI, a BLSOM (batch-learning SOM), which can analyze five million genomic sequences simultaneously.  ...  SARS-CoV-2 poses a serious threat to public health and one important issue in characterizing this fast-evolving virus is to elucidate various aspects of their genome sequence changes.  ...  We previously established a BLSOM (batch-learning self-organizing map) for oligonucleotide compositions, which can reveal various new characteristics of genome sequences [4, 5] .  ... 
doi:10.1186/s12866-022-02484-3 pmid:35272618 pmcid:PMC8907386 fatcat:exa4pw2ewjbjhk5ofgznkskxlq

Comprehensive Analysis Of Data Mining Tools

S. Sarumathi, N. Shanthi
2015 Zenodo  
Due to the fast and flawless technological innovation there is a tremendous amount of data dumping all over the world in every domain such as Pattern Recognition, Machine Learning, Spatial Data Mining,  ...  In this survey the diverse tools are illustrated with their extensive technical paradigm, outstanding graphical interface and inbuilt multipath algorithms in which it is very useful for handling significant  ...  DATABIONIC The Databionic Emergent Self-Organizing Map tool [3] is a collection of programs to do data mining tasks such as visualization, clustering and classification.  ... 
doi:10.5281/zenodo.1109306 fatcat:rser2mblkzfwdcbi2xdqtpcrae

Reprint of : Application cases of biological transformation in manufacturing technology

Thomas Bergs, Ulrich Schwaneberg, Sebastian Barth, L. Hermann, Tim Grunwald, S. Mayer, F. Biermann, Nursen Sözer
2021 CIRP journal of manufacturing science and technology : CIRP-JMST 34  
The interactions of the three system types are described in detail, and their potential for the manufacturing sector is discussed in reference to the framework Biological Transformation in manufacturing  ...  This cell type can be differentiated into any other human cell type and makes it possible to work with the patient's own cell material to grow new tissues, or in future to even "grow" whole organs using  ...  An initial map of the fitness landscape can be generated using novel diversity-maximizing evolutionary algorithms.  ... 
doi:10.18154/rwth-2021-10830 fatcat:cnodapkpfnd7jl3s3jjqubm2hq

Multiple-Kernel Based Vehicle Tracking Using 3D Deformable Model and Camera Self-Calibration [article]

Zheng Tang, Gaoang Wang, Tao Liu, Young-Gun Lee, Adwin Jahn, Xu Liu, Xiaodong He, Jenq-Neng Hwang
2017 arXiv   pre-print
To build 3D car models in a fully unsupervised manner, we also implement evolutionary camera self-calibration from tracking of walking humans to automatically compute camera parameters.  ...  We combine the results from SSD and YOLO9000 based on ensemble learning.  ...  Then we combine the detection results from SSD and YOLO9000 according to ensemble learning. The rest of this paper is organized as follows.  ... 
arXiv:1708.06831v1 fatcat:wdjkhfvahbel7owg35j7pikxyi

Optimal Path Planning for Intelligent UAVs Using Graph Convolution Networks

Akshya Jothi, P. L. K. Priyadarsini
2022 Intelligent Automation and Soft Computing  
The model is also evaluated against the performance of evolutionary algorithms on several self-constructed graphs.  ...  A graph can be constructed from the map of the area under surveillance, using computational geometric techniques.  ...  Adam optimizer was used with an initial learning rate of 1e-3 to reduce the overall crossentropy loss occurring in each mini-batch.  ... 
doi:10.32604/iasc.2022.020974 fatcat:iijpq74zp5e2llyqbcticpo4sa
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