Application of Parallel Distributed Implementation to Multiobjective Fuzzy Genetics-Based Machine Learning

2015 ◽  
pp. 462-471 ◽  
Author(s):  
Yusuke Nojima ◽  
Yuji Takahashi ◽  
Hisao Ishibuchi
Keyword(s):  
Machine Learning ◽  
Distributed Implementation

ActorNode2Vec: An Actor-based solution for Node Embedding over large networks

2020 ◽  
Vol 14 (1) ◽  
pp. 103-114
Author(s):  
Gianfranco Lombardo ◽  
Agostino Poggi
Keyword(s):  
Machine Learning ◽  
Time Complexity ◽  
Language Models ◽  
Machine Learning Techniques ◽  
Large Network ◽  
Actor Model ◽  
Research Fields ◽  
Learning Techniques ◽  
Wide Range ◽  
Distributed Implementation

 The application of Machine Learning techniques over networks, such as prediction tasks over nodes and edges, is becoming often crucial in the analysis of Complex systems in a wide range of research fields. One of the enabling technologies in that sense is represented by Node Embedding, which enables us to learn features automatically over the network. Among the different approaches proposed in the literature, the most promising are DeepWalk and Node2Vec, where the embedding is computed by combining random walks and neural language models. However, characteristic limitations with these techniques are related to memory requirements and time complexity. In this paper, we propose a distributed and scalable solution, named ActorNode2vec, that keeps the best advantages of Node2Vec and overcomes the limitations with the adoption of the actor model to distribute the computational load. We demonstrate the efficacy of this approach with a large network by analyzing the sensitivity of walk length and number of walks parameters and make a comparison also with Deep walk and an Apache Spark distributed implementation of Node2Vec. Results show that with ActorNode2vec computational times are drastically reduced without losing embedding quality and overcoming memory issues.

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

Machine Learning for Speaker Recognition

2020 ◽  
Author(s):  
Man-Wai Mak ◽  
Jen-Tzung Chien
Keyword(s):  
Machine Learning ◽  
Speaker Recognition

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining

Mathematics for Machine Learning

2020 ◽  
Author(s):  
Marc Peter Deisenroth ◽  
A. Aldo Faisal ◽  
Cheng Soon Ong
Keyword(s):  
Machine Learning

Phase Transitions in Machine Learning

2009 ◽  
Author(s):  
Lorenza Saitta ◽  
Attilio Giordana ◽  
Antoine Cornuejols
Keyword(s):  
Machine Learning ◽  
Phase Transitions

Understanding Machine Learning

2009 ◽  
Author(s):  
Shai Shalev-Shwartz ◽  
Shai Ben-David
Keyword(s):  
Machine Learning
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