Mapping Geometric and Electromagnetic Feature Spaces with Machine Learning for Additively Manufactured RF Devices

2021 ◽  
pp. 102549
Author(s):  
Deanna Sessions ◽  
Venkatesh Meenakshisundaram ◽  
Andrew Gillman ◽  
Alexander Cook ◽  
Kazuko Fuchi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Spaces ◽  
Rf Devices

scholarly journals Classification of Photogrammetric and Airborne LiDAR Point Clouds Using Machine Learning Algorithms

Drones ◽  
2021 ◽  
Vol 5 (4) ◽  
pp. 104
Author(s):  
Zaide Duran ◽  
Kubra Ozcan ◽  
Muhammed Enes Atik
Keyword(s):  
Machine Learning ◽  
Point Cloud ◽  
Point Clouds ◽  
Machine Learning Algorithms ◽  
Airborne Lidar ◽  
Aerial Photogrammetry ◽  
Feature Spaces ◽  
Wide Range ◽  
Extract Information

With the development of photogrammetry technologies, point clouds have found a wide range of use in academic and commercial areas. This situation has made it essential to extract information from point clouds. In particular, artificial intelligence applications have been used to extract information from point clouds to complex structures. Point cloud classification is also one of the leading areas where these applications are used. In this study, the classification of point clouds obtained by aerial photogrammetry and Light Detection and Ranging (LiDAR) technology belonging to the same region is performed by using machine learning. For this purpose, nine popular machine learning methods have been used. Geometric features obtained from point clouds were used for the feature spaces created for classification. Color information is also added to these in the photogrammetric point cloud. According to the LiDAR point cloud results, the highest overall accuracies were obtained as 0.96 with the Multilayer Perceptron (MLP) method. The lowest overall accuracies were obtained as 0.50 with the AdaBoost method. The method with the highest overall accuracy was achieved with the MLP (0.90) method. The lowest overall accuracy method is the GNB method with 0.25 overall accuracy.

scholarly journals An Effective Multi-Label Feature Selection Model Towards Eliminating Noisy Features

2020 ◽  
Vol 10 (22) ◽  
pp. 8093
Author(s):  
Jun Wang ◽  
Yuanyuan Xu ◽  
Hengpeng Xu ◽  
Zhe Sun ◽  
Zhenglu Yang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Learning Performance ◽  
Space Structures ◽  
Learning Tasks ◽  
Feature Spaces ◽  
Selection Approach ◽  
Label Correlations ◽  
Feature Selection Approach ◽  
Low Dimensional

Feature selection has devoted a consistently great amount of effort to dimension reduction for various machine learning tasks. Existing feature selection models focus on selecting the most discriminative features for learning targets. However, this strategy is weak in handling two kinds of features, that is, the irrelevant and redundant ones, which are collectively referred to as noisy features. These features may hamper the construction of optimal low-dimensional subspaces and compromise the learning performance of downstream tasks. In this study, we propose a novel multi-label feature selection approach by embedding label correlations (dubbed ELC) to address these issues. Particularly, we extract label correlations for reliable label space structures and employ them to steer feature selection. In this way, label and feature spaces can be expected to be consistent and noisy features can be effectively eliminated. An extensive experimental evaluation on public benchmarks validated the superiority of ELC.

AUTHORSHIP ATTRIBUTION BASED ON FEATURE SET SUBSPACING ENSEMBLES

2006 ◽  
Vol 15 (05) ◽  
pp. 823-838 ◽  
Author(s):  
EFSTATHIOS STAMATATOS
Keyword(s):  
Machine Learning ◽  
Text Categorization ◽  
Machine Learning Techniques ◽  
Authorship Attribution ◽  
Support Vector ◽  
Classifier Ensembles ◽  
Simple Method ◽  
Text Corpora ◽  
Learning Techniques ◽  
Feature Spaces

Authorship attribution can assist the criminal investigation procedure as well as cybercrime analysis. This task can be viewed as a single-label multi-class text categorization problem. Given that the style of a text can be represented as mere word frequencies selected in a language-independent method, suitable machine learning techniques able to deal with high dimensional feature spaces and sparse data can be directly applied to solve this problem. This paper focuses on classifier ensembles based on feature set subspacing. It is shown that an effective ensemble can be constructed using, exhaustive disjoint subspacing, a simple method producing many poor but diverse base classifiers. The simple model can be enhanced by a variation of the technique of cross-validated committees applied to the feature set. Experiments on two benchmark text corpora demonstrate the effectiveness of the presented method improving previously reported results and compare it to support vector machines, an alternative suitable machine learning approach to authorship attribution.

scholarly journals Experimental quantum kernel trick with nuclear spins in a solid

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Takeru Kusumoto ◽  
Kosuke Mitarai ◽  
Keisuke Fujii ◽  
Masahiro Kitagawa ◽  
Makoto Negoro
Keyword(s):  
Machine Learning ◽  
Feature Space ◽  
Learning Task ◽  
Quantum Systems ◽  
Kernel Functions ◽  
Single Shot ◽  
Nuclear Spins ◽  
Kernel Trick ◽  
Kernel Machine ◽  
Feature Spaces

AbstractThe kernel trick allows us to employ high-dimensional feature space for a machine learning task without explicitly storing features. Recently, the idea of utilizing quantum systems for computing kernel functions using interference has been demonstrated experimentally. However, the dimension of feature spaces in those experiments have been smaller than the number of data, which makes them lose their computational advantage over explicit method. Here we show the first experimental demonstration of a quantum kernel machine that achieves a scheme where the dimension of feature space greatly exceeds the number of data using 1H nuclear spins in solid. The use of NMR allows us to obtain the kernel values with single-shot experiment. We employ engineered dynamics correlating 25 spins which is equivalent to using a feature space with a dimension over 1015. This work presents a quantum machine learning using one of the largest quantum systems to date.

An integrated approach for different attribute types in nearest neighbour classification

1996 ◽  
Vol 11 (3) ◽  
pp. 245-252
Author(s):  
W. Z. Liu
Keyword(s):  
Machine Learning ◽  
Euclidean Distance ◽  
Integrated Approach ◽  
Nearest Neighbour ◽  
Distance Metric ◽  
Classification Techniques ◽  
Feature Spaces ◽  
Classification Tasks ◽  
Combined Work ◽  
Mixed Types

AbstractThe basic nearest neighbour algorithm works by storing the training instances and classifying a new case by predicting that it has the same class as its nearest stored instance. To measure the distance between instances, some distance metric needs to be used. In situations when all attributes have numeric values, the conventional nearest neighbour method treats examples as points in feature spaces and uses Euclidean distance as the distance metric. In tasks with only nominal attributes, the simple “over-lap” metric is usually used. To handle classification tasks that have mixed types of attributes, the two different metrics are simply combined. Work by researchers in the machine learning field has shown that this approach performs poorly. This paper attempts to study a more recently developed distance metric and show that this metric is capable of measuring the importance of different attributes. With the use of discretisation for numeric-valued attributes, this method provides an integrated way in dealing with problem domains with mixtures of attribute types. Through detailed analyses, this paper tries to provide further insights into the understanding of nearest neighbour classification techniques and promote further use of this type of classification algorithm.

scholarly journals Survey on graph embeddings and their applications to machine learning problems on graphs

2021 ◽  
Vol 7 ◽  
pp. e357
Author(s):  
Ilya Makarov ◽  
Dmitrii Kiselev ◽  
Nikita Nikitinsky ◽  
Lovro Subelj
Keyword(s):  
Machine Learning ◽  
Link Prediction ◽  
Graph Embedding ◽  
Learning Problems ◽  
Feature Engineering ◽  
Graph Embeddings ◽  
Feature Spaces ◽  
Wide Range ◽  
Depth Analysis ◽  
Node Classification

Dealing with relational data always required significant computational resources, domain expertise and task-dependent feature engineering to incorporate structural information into a predictive model. Nowadays, a family of automated graph feature engineering techniques has been proposed in different streams of literature. So-called graph embeddings provide a powerful tool to construct vectorized feature spaces for graphs and their components, such as nodes, edges and subgraphs under preserving inner graph properties. Using the constructed feature spaces, many machine learning problems on graphs can be solved via standard frameworks suitable for vectorized feature representation. Our survey aims to describe the core concepts of graph embeddings and provide several taxonomies for their description. First, we start with the methodological approach and extract three types of graph embedding models based on matrix factorization, random-walks and deep learning approaches. Next, we describe how different types of networks impact the ability of models to incorporate structural and attributed data into a unified embedding. Going further, we perform a thorough evaluation of graph embedding applications to machine learning problems on graphs, among which are node classification, link prediction, clustering, visualization, compression, and a family of the whole graph embedding algorithms suitable for graph classification, similarity and alignment problems. Finally, we overview the existing applications of graph embeddings to computer science domains, formulate open problems and provide experiment results, explaining how different networks properties result in graph embeddings quality in the four classic machine learning problems on graphs, such as node classification, link prediction, clustering and graph visualization. As a result, our survey covers a new rapidly growing field of network feature engineering, presents an in-depth analysis of models based on network types, and overviews a wide range of applications to machine learning problems on graphs.

Feature Selection Algorithm Considering Trial and Individual Differences for Machine Learning of Human Activity Recognition

2017 ◽  
Vol 21 (5) ◽  
pp. 813-824 ◽  
Author(s):  
Yuto Omae ◽  
Hirotaka Takahashi ◽  
◽  
Keyword(s):  
Machine Learning ◽  
Human Body ◽  
Evaluation Method ◽  
Probability Distributions ◽  
Feature Space ◽  
Training Data ◽  
Sensor Data ◽  
Support Vector ◽  
Feature Spaces ◽  
Generalization Errors

In recent years, many studies have been performed on the automatic classification of human body motions based on inertia sensor data using a combination of inertia sensors and machine learning; training data is necessary where sensor data and human body motions correspond to one another. It can be difficult to conduct experiments involving a large number of subjects over an extended time period, because of concern for the fatigue or injury of subjects. Many studies, therefore, allow a small number of subjects to perform repeated body motions subject to classification, to acquire data on which to build training data. Any classifiers constructed using such training data will have some problems associated with generalization errors caused by individual and trial differences. In order to suppress such generalization errors, feature spaces must be obtained that are less likely to generate generalization errors due to individual and trial differences. To obtain such feature spaces, we require indices to evaluate the likelihood of the feature spaces generating generalization errors due to individual and trial errors. This paper, therefore, aims to devise such evaluation indices from the perspectives. The evaluation indices we propose in this paper can be obtained by first constructing acquired data probability distributions that represent individual and trial differences, and then using such probability distributions to calculate any risks of generating generalization errors. We have verified the effectiveness of the proposed evaluation method by applying it to sensor data for butterfly and breaststroke swimming. For the purpose of comparison, we have also applied a few available existing evaluation methods. We have constructed classifiers for butterfly and breaststroke swimming by applying a support vector machine to the feature spaces obtained by the proposed and existing methods. Based on the accuracy verification we conducted with test data, we found that the proposed method produced significantly higher F-measure than the existing methods. This proves that the use of the proposed evaluation indices enables us to obtain a feature space that is less likely to generate generalization errors due to individual and trial differences.

scholarly journals A machine learning approach to textual entailment recognition

2009 ◽  
Vol 15 (4) ◽  
pp. 551-582 ◽  
Author(s):  
FABIO MASSIMO ZANZOTTO ◽  
MARCO PENNACCHIOTTI ◽  
ALESSANDRO MOSCHITTI
Keyword(s):  
Machine Learning ◽  
Machine Learning Algorithms ◽  
Semantic Relations ◽  
Supervised Machine Learning ◽  
First Order ◽  
Feature Spaces ◽  
Machine Learning Approach ◽  
Textual Entailment ◽  
Rewrite Rules ◽  
Do So

AbstractDesigning models for learning textual entailment recognizers from annotated examples is not an easy task, as it requires modeling the semantic relations and interactions involved between two pairs of text fragments. In this paper, we approach the problem by first introducing the class of pair feature spaces, which allow supervised machine learning algorithms to derive first-order rewrite rules from annotated examples. In particular, we propose syntactic and shallow semantic feature spaces, and compare them to standard ones. Extensive experiments demonstrate that our proposed spaces learn first-order derivations, while standard ones are not expressive enough to do so.

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