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Constructor of compositions of machine learning models for solving classification problems

Igor Lavrov, Jenny Domashova
2020 Procedia Computer Science  
The use of the proposed tools improves the accuracy of solving the classification problem on input data in the context of various subject areas through the utilization of a composition of machine learning  ...  The use of the proposed tools improves the accuracy of solving the classification problem on input data in the context of various subject areas through the utilization of a composition of machine learning  ...  The product should provide an opportunity to build and train machine learning models on the selected train-sample, as well as composition of machine learning models, in which machine learning algorithms  ... 
doi:10.1016/j.procs.2020.02.165 fatcat:j5twzhf6wnh2tdcusj37bjfpfy

Analysis on the Man-Machine-Environment Collaborative Teaching Method for Mining Engineering Major

Lang Liu, Xuehua Sun, Ki-il Song
2016 International Journal of Emerging Technologies in Learning (iJET)  
Also, we conducted a comprehensive discussion about the academic problems in various aspects of composition, theoretical basis, and functional allocation by combining teaching reform features of the mining  ...  The practice of man–machine–environment collaborative teaching method strengthens students' learning of perceptual knowledge, alleviates difficulty of site visit and practice, and enables conducting an  ...  Figure 1 . 1 Combined model of man-machine-environment and higher education systems Figure 3 . 3 Composition and functions of "man" in the teaching system of the mining engineering Figure 4 . 4 Composition  ... 
doi:10.3991/ijet.v11i10.6269 fatcat:gkt7pbmubvfprnxm34oco3y6t4

MLaaS: Machine Learning as a Service

Mauro Ribeiro, Katarina Grolinger, Miriam A.M. Capretz
2015 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)  
Machine learning has been gaining much attention in data mining, leveraging the birth of new solutions.  ...  This paper proposes an architecture to create a flexible and scalable machine learning as a service. An open source solution was implemented and presented.  ...  The architecture works as follows: the Machine Learning as a Service composite receives raw data from data sources through its Send Training Set service.  ... 
doi:10.1109/icmla.2015.152 dblp:conf/icmla/RibeiroGC15 fatcat:upnysbo75jegnem2bzrttd2yu4

Application in Composite Machine Using RBF Neural Network Based on PID Control

Jia Chunying
2014 Automation Control and Intelligent Systems  
Analyzed the reason and the tension control mathematical model generation composite machine tension in the BOPP production line, for the constant tension control of composite machine, put forward a kind  ...  In the absence of solvent composite machine, because the radius of drum winding and rewinding roller in the transmission process is changing.  ...  The hidden layer nodes by like Gauss function radial function as the composition, the number of nodes do not need to like the BP network that are set in advance, but increased in the learning process until  ... 
doi:10.11648/j.acis.20140206.11 fatcat:5adstmbdujew3l3ybgmciuisrq

Research and Design of Automatic Scoring Algorithm for English Composition Based on Machine Learning

Yu Zhao, Baiyuan Ding
2021 Scientific Programming  
Therefore, this paper constructs the automatic scoring algorithm and sentence elegance feature scoring algorithm of English composition based on machine learning, explores the influence of the algorithm  ...  With the development of artificial intelligence and big data, the concept of "Internet plus education" has gradually become popular, including automatic scoring system based on machine learning.  ...  By training neural network language model through unsupervised learning, semantic information contained in the text could be obtained.  ... 
doi:10.1155/2021/3429463 fatcat:ux6o6rvrsrhspnrcjcgbwycefa

Machine Learning for Materials Developments in Metals Additive Manufacturing [article]

N. S. Johnson, P. S. Vulimiri, A. C. To, X. Zhang, C. A. Brice, B. B. Kappes, A. P. Stebner
2020 arXiv   pre-print
This materials technologies-focused review introduces the basic mathematics and terminology of machine learning through the lens of metals AM, and then examines potential uses of machine learning to advance  ...  New machine learning techniques are not needed for most metals AM development; those used in other sects of materials science will also work for AM.  ...  R Machine Learning in R (MLR) [63] Infrastructure for incorporating common machine learning functions in R in an easy way; provides robust packages for a wide range of machine learning-based tools including  ... 
arXiv:2005.05235v1 fatcat:55w65jx5bjf6vk3o25zwp4wzbi

Inferring Affordances Using Learning Techniques [chapter]

Amel Bennaceur, Richard Johansson, Alessandro Moschitti, Romina Spalazzese, Daniel Sykes, Rachid Saadi, Valérie Issarny
2012 Communications in Computer and Information Science  
To overcome this, we propose to use machine learning to extract the high-level functionality of a system and thus restrict the scope of detailed analysis to systems likely to be able to interoperate.  ...  Introduction to machine learning In general, we define machine learning as the problem of inducing a function (or system of functions) from a given data set.  ...  The first step consists of checking the compatibility of their affordances, high-level functionality, through the use of semantic matching ().  ... 
doi:10.1007/978-3-642-28033-7_7 fatcat:ane57rdbz5b2hhr7x5tacklsgu

Motivation Learning in Mind Model CAM

Zhongzhi Shi, Gang Ma, Xi Yang, Chengxiang Lu
2015 International Journal of Intelligence Science  
Motivation learning aims to create abstract motivations and related goals. It is one of the highlevel cognitive functions in Consciousness And Memory model (CAM).  ...  This paper proposes a new motivation learning algorithm which allows an agent to create motivations or goals based on introspective process.  ...  Through learning, humans, animals and some machines acquire new, or modify and reinforce existing knowledge, behaviors, skills, values, or preferences.  ... 
doi:10.4236/ijis.2015.52006 fatcat:nklrdhakjba7dbmehienri2cc4

PClass: Protein Quaternary Structure Classification by Using Bootstrapping Strategy as Model Selection

Chi-Chou Huang, Chi-Chang Chang, Chi-Wei Chen, Shao-yu Ho, Hsung-Pin Chang, Yen-Wei Chu
2018 Genes  
Therefore, we designed the architecture of a two-layer machine learning technique in this study, and developed the classification system PClass.  ...  The second layer of construction combines the first-layer module to integrate mechanisms and the use of six machine learning methods to improve the prediction performance.  ...  The trimeric class used machine learning methods, and the best machine learning method was selected (Supplementary Figure S7) .  ... 
doi:10.3390/genes9020091 pmid:29443925 pmcid:PMC5852587 fatcat:er67hz4qxzaw7frxo5mcpzqrte

Ontology Based Dynamic e-Learning Flow Composition of Learning Web Services

M. Farida Begam, Gopinath Ganapathy
2014 Research Journal of Applied Sciences Engineering and Technology  
Identifying required e-learning web services and dynamic composition and realization of those services is a challenging process.  ...  Web Services has instigated it's transcend and now education has been made simple through Web Services.  ...  In Semantic Web information has machine-processed and machine-understandable semantics that enhancing the machine readability of web content.  ... 
doi:10.19026/rjaset.7.858 fatcat:waqwt5nxkfgvtnzzij34gectgq

Machine learned regression for abductive DNA sequencing

David J. Thornley, Maxim Zverev, Stavros Petridis
2007 Sixth International Conference on Machine Learning and Applications (ICMLA 2007)  
We machine learn a means for comparing the measures taken from competing hypotheses for the sequence. This is a machine learned implementation of our proposal for abductive DNA basecalling.  ...  These predictions are used to assess hypotheses for sequence composition through calculation of likelihood or deviation evidence from the comparison of predictions from the hypothesized sequence with target  ...  Our hope is that a machine learned entity can capture this in a helpful manner. To train such a machine learned entity, we require a corpus of examples for it to summarize as a function.  ... 
doi:10.1109/icmla.2007.33 dblp:conf/icmla/ThornleyZP07 fatcat:upt4nql5m5al3nbmz5xw4pnaoy

Artificial intelligence and abdominal adipose tissue analysis: a literature review

Federico Greco, Carlo Augusto Mallio
2021 Quantitative Imaging in Medicine and Surgery  
Body composition imaging relies on assessment of tissues composition and distribution.  ...  Manual segmentation of imaging data allows to obtain information on abdominal adipose tissue; however, this procedure can be cumbersome and time-consuming.  ...  noncommercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through  ... 
doi:10.21037/qims-21-370 pmid:34603998 pmcid:PMC8408793 fatcat:4ymzwcw36jdqhdmxhj2usudodm

Brainish: Formalizing A Multimodal Language for Intelligence and Consciousness [article]

Paul Pu Liang
2022 arXiv   pre-print
for processing and relating information from heterogeneous signals.  ...  Building upon the Conscious Turing Machine (CTM), a machine model for consciousness proposed by Blum and Blum (2021), we describe the desiderata of a multimodal language called Brainish, comprising words  ...  Acknowledgements PPL is supported in part by a Facebook PhD Fellowship and a Carnegie Mellon University's Center for Machine Learning and Health Fellowship.  ... 
arXiv:2205.00001v2 fatcat:hnqtq5cer5bxhetyuctifjt5za

Towards automated design of corrosion resistant alloy coatings with an autonomous scanning droplet cell [article]

Brian DeCost, Howie Joress, Suchismita Sarker, Apurva Mehta, Jason Hattrick-Simpers
2022 arXiv   pre-print
This motivates a close coupling between autonomous research platforms and scientific machine learning methodology that blends mechanistic physical models and black box machine learning models.  ...  Automation and machine learning are currently driving rapid innovation in high throughput and autonomous materials design and discovery.  ...  adaptive machine learning systems for data evaluation and experimental planning.  ... 
arXiv:2203.17049v1 fatcat:jp5td2shcbdwrdpdep4ia5lzqa

Compositional Dynamics: Defining the Fuzzy Cell

Georg Kustatscher, Juri Rappsilber
2016 Trends in Cell Biology  
Acknowledgments We would like to thank Carl Wu for prompting this manuscript through his questions at the Gordon  ...  The clue might reside in compositional changes that follow biological challenges and that can be decoded by machine learning.  ...  One potential way of achieving this is to use machine-learning algorithms to integrate a variety of data sources that include this information (Box 1).  ... 
doi:10.1016/j.tcb.2016.08.012 pmid:27651031 pmcid:PMC5080450 fatcat:3xovjagoifhvbkzmfxkdcw56ya
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