scholarly journals An empirical feature-based learning algorithm producing sparse approximations

2012 ◽  
Vol 32 (3) ◽  
pp. 389-400 ◽  
Author(s):  
Xin Guo ◽  
Ding-Xuan Zhou
Keyword(s):  
Learning Algorithm ◽  
Sparse Approximations ◽  
Feature Based

Graph Classification Using Back Propagation Learning Algorithms

2020 ◽  
Vol 11 (2) ◽  
pp. 1-12
Author(s):  
Abhijit Bera ◽  
Mrinal Kanti Ghose ◽  
Dibyendu Kumar Pal
Keyword(s):  
Learning Algorithm ◽  
Back Propagation ◽  
Computational Time ◽  
Classification Algorithms ◽  
Graph Classification ◽  
Graph Data ◽  
Computational Overhead ◽  
Sharp Focus ◽  
Feature Based ◽  
Selection Algorithms

Due to the propagation of graph data, there has been a sharp focus on developing effective methods for classifying the graph object. As most of the proposed graph classification techniques though effective are constrained by high computational overhead, there is a consistent effort to improve upon the existing classification algorithms in terms of higher accuracy and less computational time. In this paper, an attempt has been made to classify graphs by extracting various features and selecting the important features using feature selection algorithms. Since all the extracted graph-based features need not be equally important, only the most important features are selected by using back propagation learning algorithm. The results of the proposed study of feature-based approach using back propagation learning algorithm lead to higher classification accuracy with faster computational time in comparison to other graph kernels. It also appears to be more effective for large unlabeled graphs.

scholarly journals Proximity Environmental Feature Based Tree Health Assessment Scheme Using Internet of Things and Machine Learning Algorithm

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3115 ◽  
Author(s):  
Yang Wei ◽  
Hao Wang ◽  
Kim Fung Tsang ◽  
Yucheng Liu ◽  
Chung Kit Wu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Learning Algorithm ◽  
Rbf Neural Network ◽  
Health Assessment ◽  
Sensor Data ◽  
Machine Learning Algorithm ◽  
Tree Health ◽  
Environmental Feature ◽  
Feature Based

Improperly grown trees may cause huge hazards to the environment and to humans, through e.g., climate change, soil erosion, etc. A proximity environmental feature-based tree health assessment (PTA) scheme is proposed to prevent these hazards by providing guidance for early warning methods of potential poor tree health. In PTA development, tree health is defined and evaluated based on proximity environmental features (PEFs). The PEF takes into consideration the seven surrounding ambient features that strongly impact tree health. The PEFs were measured by the deployed smart sensors surrounding trees. A database composed of tree health and relative PEFs was established for further analysis. An adaptive data identifying (ADI) algorithm is applied to exclude the influence of interference factors in the database. Finally, the radial basis function (RBF) neural network (NN), a machine leaning algorithm, has been identified as the appropriate tool with which to correlate tree health and PEFs to establish the PTA algorithm. One of the salient features of PTA is that the algorithm can evaluate, and thus monitor, tree health remotely and automatically from smart sensor data by taking advantage of the well-established internet of things (IoT) network and machine learning algorithm.

Streaming feature-based causal structure learning algorithm with symmetrical uncertainty

2018 ◽  
Vol 467 ◽  
pp. 708-724 ◽  
Author(s):  
Jing Yang ◽  
Xiaoxue Guo ◽  
Ning An ◽  
Aiguo Wang ◽  
Kui Yu
Keyword(s):  
Structure Learning ◽  
Learning Algorithm ◽  
Causal Structure ◽  
Symmetrical Uncertainty ◽  
Feature Based

scholarly journals A LEARNING METHOD TO DETERMINE HOW TO APPROACH AN UNKNOWN OBJECT TO BE GRASPED

2011 ◽  
Vol 08 (03) ◽  
pp. 579-606 ◽  
Author(s):  
BENJAMIN D. BALAGUER ◽  
STEFANO CARPIN
Keyword(s):  
Learning Algorithm ◽  
Training Session ◽  
Training Data ◽  
Support Vector ◽  
The Novel ◽  
Learning Method ◽  
End Effector ◽  
Novel Object ◽  
Time Performance ◽  
Feature Based

We present a learning algorithm to determine the appropriate approaching pose to grasp a novel object. Our method focuses on the computation of valid end-effector orientations in order to make contact with the object at a given point. The system achieves this goal by generalizing from positive examples provided by a human operator during an offline training session. The technique is feature-based since it extracts salient attributes of the object to be grasped rather than relying on the availability of models or trying to build one. To compute the desired orientation, the robot performs three steps at run time. Using a multi-class Support Vector Machine (SVM), it first classifies the novel object into one of the object classes defined during training. Next, it determines its orientation, and, finally, based on the classification and orientation, it extracts the most similar example from the training data and uses it to grasp the object. The method has been implemented on a full-scale humanoid robotic torso equipped with multi-fingered hands and extensive results corroborate both its effectiveness and real-time performance.

Intelligent Design Prediction Aided by Non-Uniform Parametric Study and Machine Learning in Feature Based Product Development

2021 ◽  
Author(s):  
Satchit Ramnath ◽  
Jiachen Ma ◽  
Jami J. Shah ◽  
Duane Detwiler
Keyword(s):  
Machine Learning ◽  
Parametric Study ◽  
Parametric Design ◽  
Learning Algorithm ◽  
Structure Design ◽  
Data Sets ◽  
Element Analysis ◽  
Automotive Body ◽  
Current Design ◽  
Feature Based

Abstract Automotive body structure design is critical to achieve lightweight and crash worthiness based on engineers’ experience. In the current design process, it frequently occurs that designers use a previous generation design to evolve the latest designs to meet certain targets. However, in this process the possibility of adapting design ideas from other models is unlikely. The uniqueness of each design and presence of non-uniform parameters further makes it difficult to compare two or more designs and extract useful feature information. There is a need for a method that will fill the missing gap in assisting designers with better design options. This paper aims to fill this gap by introducing an innovative approach to use a non-uniform parametric study with machine learning in order to make valuable suggestions to the designer. The proposed method uses data sets produced from experiment design to reduce the number of parameters, perform parameter correlation studies and run finite element analysis (FEA), for a given set of loads. The response data generated from this FEA is then used in a machine learning algorithm to make predictions on the ideal features to be used in the design. The method can be applied to any component that has a feature-based parametric design.

An improved technique for face recognition applications

2018 ◽  
Vol 119 (9/10) ◽  
pp. 529-544 ◽  
Author(s):  
Ihab Zaqout ◽  
Mones Al-Hanjori
Keyword(s):  
Face Recognition ◽  
Computer Games ◽  
Recognition Accuracy ◽  
Learning Algorithm ◽  
Recognition Rate ◽  
Data Set ◽  
Content Type ◽  
Linear Discriminant ◽  
The Face ◽  
Feature Based

Purpose The face recognition problem has a long history and a significant practical perspective and one of the practical applications of the theory of pattern recognition, to automatically localize the face in the image and, if necessary, identify the person in the face. Interests in the procedures underlying the process of localization and individual’s recognition are quite significant in connection with the variety of their practical application in such areas as security systems, verification, forensic expertise, teleconferences, computer games, etc. This paper aims to recognize facial images efficiently. An averaged-feature based technique is proposed to reduce the dimensions of the multi-expression facial features. The classifier model is generated using a supervised learning algorithm called a back-propagation neural network (BPNN), implemented on a MatLab R2017. The recognition rate and accuracy of the proposed methodology is comparable with other methods such as the principle component analysis and linear discriminant analysis with the same data set. In total, 150 faces subjects are selected from the Olivetti Research Laboratory (ORL) data set, resulting 95.6 and 85 per cent recognition rate and accuracy, respectively, and 165 faces subjects from the Yale data set, resulting 95.5 and 84.4 per cent recognition rate and accuracy, respectively. Design/methodology/approach Averaged-feature based approach (dimension reduction) and BPNN (generate supervised classifier). Findings The recognition rate is 95.6 per cent and recognition accuracy is 85 per cent for the ORL data set, whereas the recognition rate is 95.5 per cent and recognition accuracy is 84.4 per cent for the Yale data set. Originality/value Averaged-feature based method.

scholarly journals Closed-Loop Learning of Visual Control Policies

2007 ◽  
Vol 28 ◽  
pp. 349-391 ◽  
Author(s):  
S. R. Jodogne ◽  
J. H. Piater
Keyword(s):  
Learning Algorithm ◽  
Visual Space ◽  
Visual Navigation ◽  
Visual Features ◽  
Local Descriptors ◽  
Perceptual Aliasing ◽  
Feature Based ◽  
Flexible Framework ◽  
High Level ◽  
The Hill

In this paper we present a general, flexible framework for learning mappings from images to actions by interacting with the environment. The basic idea is to introduce a feature-based image classifier in front of a reinforcement learning algorithm. The classifier partitions the visual space according to the presence or absence of few highly informative local descriptors that are incrementally selected in a sequence of attempts to remove perceptual aliasing. We also address the problem of fighting overfitting in such a greedy algorithm. Finally, we show how high-level visual features can be generated when the power of local descriptors is insufficient for completely disambiguating the aliased states. This is done by building a hierarchy of composite features that consist of recursive spatial combinations of visual features. We demonstrate the efficacy of our algorithms by solving three visual navigation tasks and a visual version of the classical ``Car on the Hill'' control problem.

scholarly journals Automatic Hate Speech Detection in English-Odia Code Mixed Social Media Data Using Machine Learning Techniques

2021 ◽  
Vol 11 (18) ◽  
pp. 8575
Author(s):  
Sudhir Kumar Mohapatra ◽  
Srinivas Prasad ◽  
Dwiti Krishna Bebarta ◽  
Tapan Kumar Das ◽  
Kathiravan Srinivasan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Hate Speech ◽  
Learning Algorithm ◽  
Machine Learning Techniques ◽  
Mixed Data ◽  
Support Vector ◽  
Speech Detection ◽  
Detection Model ◽  
Feature Based

Hate speech on social media may spread quickly through online users and subsequently, may even escalate into local vile violence and heinous crimes. This paper proposes a hate speech detection model by means of machine learning and text mining feature extraction techniques. In this study, the authors collected the hate speech of English-Odia code mixed data from a Facebook public page and manually organized them into three classes. In order to build binary and ternary datasets, the data are further converted into binary classes. The modeling of hate speech employs the combination of a machine learning algorithm and features extraction. Support vector machine (SVM), naïve Bayes (NB) and random forest (RF) models were trained using the whole dataset, with the extracted feature based on word unigram, bigram, trigram, combined n-grams, term frequency-inverse document frequency (TF-IDF), combined n-grams weighted by TF-IDF and word2vec for both the datasets. Using the two datasets, we developed two kinds of models with each feature—binary models and ternary models. The models based on SVM with word2vec achieved better performance than the NB and RF models for both the binary and ternary categories. The result reveals that the ternary models achieved less confusion between hate and non-hate speech than the binary models.

Toward Improved Prediction of AAA Rupture Risk: Implementation of Feature-Based Geometry Quantification Measures Compared to Maximum Diameter Alone

2012 ◽  
Author(s):  
J. Shum ◽  
S. C. Muluk ◽  
A. Doyle ◽  
A. Chandra ◽  
M. Eskandari ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Statistical Models ◽  
Learning Algorithm ◽  
Aortic Aneurysms ◽  
Maximum Diameter ◽  
Rupture Risk ◽  
Large Database ◽  
Abdominal Aortic ◽  
Feature Based

Data mining techniques are capable of extracting important relationships and correlations among large amounts of data while machine learning methodologies can utilize these correlations to generate models capable of classification and prediction. The combination of machine learning and data mining is an important contribution of the present work for two reasons: (1) given a large database of features that describe the geometry of native abdominal aortic aneurysms (AAAs), patterns and relationships in the data are derived that may not be apparent to the human eye, and (2) statistical models are generated that can classify new data and determine which features discriminate among different aneurysm populations. The objectives of this study were to use anatomically realistic AAA models to evaluate a proposed set of global geometric indices describing the size, shape and individual wall thickness of the aneurysm sac, and use a learning algorithm to develop a model that is capable of discriminating the rupture status of these aneurysms.

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