Multibranch Formal Neuron: An Internally Nonlinear Learning Unit

2021 ◽  
pp. 1-26
Author(s):  
Marifi Güler
Keyword(s):  
Formal Model ◽  
Learning Algorithm ◽  
Wide Spectrum ◽  
Model Unit ◽  
Highly Nonlinear ◽  
Unseen Data ◽  
Nonlinear Processing ◽  
Dendritic Branches ◽  
Nonlinear Learning ◽  
Synaptic Clustering

The transformation of synaptic input into action potential in nerve cells is strongly influenced by the morphology of the dendritic arbor as well as the synaptic efficacy map. The multiplicity of dendritic branches strikingly enables a single cell to act as a highly nonlinear processing element. Studies have also found functional synaptic clustering whereby synapses that encode a common sensory feature are spatially clustered together on the branches. Motivated by these findings, here we introduce a multibranch formal model of the neuron that can integrate synaptic inputs nonlinearly through collective action of its dendritic branches and yields synaptic clustering. An analysis in support of its use as a computational building block is offered. Also offered is an accompanying gradient descent–based learning algorithm. The model unit spans a wide spectrum of nonlinearities, including the parity problem, and can outperform the multilayer perceptron in generalizing to unseen data. The occurrence of synaptic clustering boosts the generalization efficiency of the unit, which may also be the answer for the puzzling ubiquity of synaptic clustering in the real neurons. Our theoretical analysis is backed up by simulations. The study could pave the way to new artificial neural networks.

scholarly journals Does Tail Label Help for Large-Scale Multi-Label Learning

2018 ◽  
Author(s):  
Tong Wei ◽  
Yu-Feng Li
Keyword(s):  
Large Scale ◽  
Performance Metrics ◽  
Learning Algorithm ◽  
Predictive Performance ◽  
Low Complexity ◽  
Tail Distribution ◽  
Prediction Time ◽  
Unseen Data ◽  
Model Size ◽  
Fast Prediction

Large-scale multi-label learning annotates relevant labels for unseen data from a huge number of candidate labels. It is well known that in large-scale multi-label learning, labels exhibit a long tail distribution in which a significant fraction of labels are tail labels. Nonetheless, how tail labels make impact on the performance metrics in large-scale multi-label learning was not explicitly quantified. In this paper, we disclose that whatever labels are randomly missing or misclassified, tail labels impact much less than common labels in terms of commonly used performance metrics (Top-$k$ precision and nDCG@$k$). With the observation above, we develop a low-complexity large-scale multi-label learning algorithm with the goal of facilitating fast prediction and compact models by trimming tail labels adaptively. Experiments clearly verify that both the prediction time and the model size are significantly reduced without sacrificing much predictive performance for state-of-the-art approaches.

scholarly journals A Learning Algorithm based on Primary School Teaching Wisdom

2010 ◽  
Vol 1 (3) ◽  
Author(s):  
Ninan Sajeeth Philip
Keyword(s):  
Data Mining ◽  
Primary School ◽  
Empirical Analysis ◽  
Teaching And Learning ◽  
Learning Algorithm ◽  
Training Data ◽  
Generalization Ability ◽  
Unseen Data ◽  
School Teaching ◽  
Mining Model

AbstractA learning algorithm based on primary school teaching and learning is presented. The methodology is to continuously evaluate the performance of the network and to train it on the examples for which they repeatedly fail, until all the examples are correctly classified. Empirical analysis on UCI data show that the algorithm produces good training data and improves the generalization ability of the network on unseen data. The algorithm has interesting applications in data mining, model evaluations and rare objects discovery.

A NEW APPROACH OF THE 2-AXES PAM ROBOT ARM IDENTIFICATION USING NEURAL MIMO NARX MODEL

2013 ◽  
Vol 22 (01) ◽  
pp. 1250039
Author(s):  
HO PHAM HUY ANH ◽  
KYOUNG KWAN AHN
Keyword(s):  
Learning Algorithm ◽  
Back Propagation ◽  
Training Data ◽  
The Novel ◽  
Robot Arm ◽  
New Approach ◽  
Identification Process ◽  
Highly Nonlinear ◽  
Model Scheme ◽  
Narx Model

In this paper, a novel MIMO Neural NARX model is used for simultaneously modeling and identifying both joints of the 2-axes PAM robot arm's inverse and forward dynamic model. The highly nonlinear cross effect of both links of the 2-axes PAM robot arm are thoroughly modeled through an Inverse and Forward Neural MIMO NARX Model-based identification process using experimental input-output training data. Consequently the proposed Inverse and Forward Neural MIMO NARX model scheme of the nonlinear 2-axes PAM robot arm has been investigated. The results show that the novel Inverse and Forward Neural MIMO NARX Model trained by Back Propagation learning algorithm yields outstanding performance and perfect accuracy.

scholarly journals Discrete Human Activity Recognition and Fall Detection by Combining FMCW RADAR Data of Heterogeneous Environments for Independent Assistive Living

Electronics ◽  
2021 ◽  
Vol 10 (18) ◽  
pp. 2237
Author(s):  
Umer Saeed ◽  
Syed Yaseen Shah ◽  
Syed Aziz Shah ◽  
Jawad Ahmad ◽  
Abdullah Alhumaidi Alotaibi ◽  
...  
Keyword(s):  
Human Activity ◽  
Human Activities ◽  
Daily Living ◽  
Continuous Wave ◽  
Learning Algorithm ◽  
Confusion Matrix ◽  
Research Work ◽  
Heterogeneous Environments ◽  
Fmcw Radar ◽  
Unseen Data

Human activity monitoring is essential for a variety of applications in many fields, particularly healthcare. The goal of this research work is to develop a system that can effectively detect fall/collapse and classify other discrete daily living activities such as sitting, standing, walking, drinking, and bending. For this paper, a publicly accessible dataset is employed, which is captured at various geographical locations using a 5.8 GHz Frequency-Modulated Continuous-Wave (FMCW) RADAR. A total of ninety-nine participants, including young and elderly individuals, took part in the experimental campaign. During data acquisition, each aforementioned activity was recorded for 5–10 s. Through the obtained data, we generated the micro-doppler signatures using short-time Fourier transform by exploiting MATLAB tools. Subsequently, the micro-doppler signatures are validated, trained, and tested using a state-of-the-art deep learning algorithm called Residual Neural Network or ResNet. The ResNet classifier is developed in Python, which is utilised to classify six distinct human activities in this study. Furthermore, the metrics used to analyse the trained model’s performance are precision, recall, F1-score, classification accuracy, and confusion matrix. To test the resilience of the proposed method, two separate experiments are carried out. The trained ResNet models are put to the test by subject-independent scenarios and unseen data of the above-mentioned human activities at diverse geographical spaces. The experimental results showed that ResNet detected the falling and rest of the daily living human activities with decent accuracy.

scholarly journals Multiparametric MRI for Characterization of the Basal Ganglia and the Midbrain

2021 ◽  
Vol 15 ◽  
Author(s):  
Till M. Schneider ◽  
Jackie Ma ◽  
Patrick Wagner ◽  
Nicolas Behl ◽  
Armin M. Nagel ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Pulse Sequence ◽  
Wide Spectrum ◽  
Magnetization Transfer Ratio ◽  
Gradient Echo ◽  
Subcortical Structures ◽  
Rapid Acquisition ◽  
Quantitative Mr ◽  
Subcortical Nuclei

Objectives To characterize subcortical nuclei by multi-parametric quantitative magnetic resonance imaging.Materials and Methods: The following quantitative multiparametric MR data of five healthy volunteers were acquired on a 7T MRI system: 3D gradient echo (GRE) data for the calculation of quantitative susceptibility maps (QSM), GRE sequences with and without off-resonant magnetic transfer pulse for magnetization transfer ratio (MTR) calculation, a magnetization−prepared 2 rapid acquisition gradient echo sequence for T1 mapping, and (after a coil change) a density-adapted 3D radial pulse sequence for 23Na imaging. First, all data were co-registered to the GRE data, volumes of interest (VOIs) for 21 subcortical structures were drawn manually for each volunteer, and a combined voxel-wise analysis of the four MR contrasts (QSM, MTR, T1, 23Na) in each structure was conducted to assess the quantitative, MR value-based differentiability of structures. Second, a machine learning algorithm based on random forests was trained to automatically classify the groups of multi-parametric voxel values from each VOI according to their association to one of the 21 subcortical structures.Results The analysis of the integrated multimodal visualization of quantitative MR values in each structure yielded a successful classification among nuclei of the ascending reticular activation system (ARAS), the limbic system and the extrapyramidal system, while classification among (epi-)thalamic nuclei was less successful. The machine learning-based approach facilitated quantitative MR value-based structure classification especially in the group of extrapyramidal nuclei and reached an overall accuracy of 85% regarding all selected nuclei.Conclusion Multimodal quantitative MR enabled excellent differentiation of a wide spectrum of subcortical nuclei with reasonable accuracy and may thus enable sensitive detection of disease and nucleus-specific MR-based contrast alterations in the future.

Performance Assessment of Learning Algorithms on Multi-Domain Data Sets

2018 ◽  
Vol 8 (1) ◽  
pp. 27-41
Author(s):  
Amit Kumar ◽  
Bikash Kanti Sarkar
Keyword(s):  
Data Mining ◽  
Performance Assessment ◽  
Learning Algorithm ◽  
Wide Spectrum ◽  
Learning Algorithms ◽  
Complex Nature ◽  
Data Sets ◽  
Real World Data ◽  
Comparative Performance ◽  
Assessment Of Learning

This article describes how for the last few decades, data mining research has had significant progress in a wide spectrum of applications. Research in prediction of multi-domain data sets is a challenging task due to the imbalanced, voluminous, conflicting, and complex nature of data sets. A learning algorithm is the most important technique for solving these problems. The learning algorithms are widely used for classification purposes. But choosing the learners that perform best for data sets of particular domains is a challenging task in data mining. This article provides a comparative performance assessment of various state-of-the-art learning algorithms over multi-domain data sets to search the effective classifier(s) for a particular domain, e.g., artificial, natural, semi-natural, etc. In the present article, a total of 14 real world data sets are selected from University of California, Irvine (UCI) machine learning repository for conducting experiments using three competent individual learners and their hybrid combinations.

Generation of wide spectrum and pedestal-free pulse compression in highly nonlinear dispersion increasing fiber

2017 ◽  
Author(s):  
Andrew A. Fotiadi ◽  
Dmitry A. Korobko ◽  
Dmitrii A. Stoliarov ◽  
Alexey A. Sysoliatin ◽  
Igor O. Zolotovskii
Keyword(s):  
Pulse Compression ◽  
Wide Spectrum ◽  
Nonlinear Dispersion ◽  
Highly Nonlinear

scholarly journals Improving Classification Performance through an Advanced Ensemble Based Heterogeneous Extreme Learning Machines

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Adnan O. M. Abuassba ◽  
Dezheng Zhang ◽  
Xiong Luo ◽  
Ahmad Shaheryar ◽  
Hazrat Ali
Keyword(s):  
Learning Algorithm ◽  
Majority Vote ◽  
Classification Performance ◽  
Training Data ◽  
Generalization Performance ◽  
Fast Learning ◽  
Unseen Data ◽  
Benchmark Datasets ◽  
Learning Machine ◽  
Hidden Layer

Extreme Learning Machine (ELM) is a fast-learning algorithm for a single-hidden layer feedforward neural network (SLFN). It often has good generalization performance. However, there are chances that it might overfit the training data due to having more hidden nodes than needed. To address the generalization performance, we use a heterogeneous ensemble approach. We propose an Advanced ELM Ensemble (AELME) for classification, which includes Regularized-ELM, L2-norm-optimized ELM (ELML2), and Kernel-ELM. The ensemble is constructed by training a randomly chosen ELM classifier on a subset of training data selected through random resampling. The proposed AELM-Ensemble is evolved by employing an objective function of increasing diversity and accuracy among the final ensemble. Finally, the class label of unseen data is predicted using majority vote approach. Splitting the training data into subsets and incorporation of heterogeneous ELM classifiers result in higher prediction accuracy, better generalization, and a lower number of base classifiers, as compared to other models (Adaboost, Bagging, Dynamic ELM ensemble, data splitting ELM ensemble, and ELM ensemble). The validity of AELME is confirmed through classification on several real-world benchmark datasets.

scholarly journals Identification Of Hepatocellular Carcinoma Using Supervised Learning Algorithms

2014 ◽  
Vol 14 (3) ◽  
pp. 5535-5542
Author(s):  
Sagri Sharma ◽  
Sanjay Kadam ◽  
Hemant Darbari
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Supervised Learning ◽  
Dna Analysis ◽  
Learning Algorithm ◽  
Expression Profiles ◽  
Learning Algorithms ◽  
Support Vector ◽  
Unseen Data ◽  
Supervised Learning Algorithms

Analysis of diseases integrating multi-factors increases the complexity of the problem and therefore, development of frameworks for the analysis of diseases is an issue that is currently a topic of intense research. Due to the inter-dependence of the various parameters, the use of traditional methodologies has not been very effective. Consequently, newer methodologies are being sought to deal with the problem. Supervised Learning Algorithms are commonly used for performing the prediction on previously unseen data. These algorithms are commonly used for applications in fields ranging from image analysis to protein structure and function prediction and they get trained using a known dataset to come up with a predictor model that generates reasonable predictions for the response to new data. Gene expression profiles generated by DNA analysis experiments can be quite complex since these experiments can involve hypotheses involving entire genomes. The application of well-known machine learning algorithm - Support Vector Machine - to analyze the expression levels of thousands of genes simultaneously in a timely, automated and cost effective way is thus used. The objectives to undertake the presented work are development of a methodology to identify genes relevant to Hepatocellular Carcinoma (HCC) from gene expression dataset utilizing supervised learning algorithms & statistical evaluations along with development of a predictive framework that can perform classification tasks on new, unseen data

scholarly journals Broiler chickens can benefit from machine learning: support vector machine analysis of observational epidemiological data

2012 ◽  
Vol 9 (73) ◽  
pp. 1934-1942 ◽  
Author(s):  
Philip J. Hepworth ◽  
Alexey V. Nefedov ◽  
Ilya B. Muchnik ◽  
Kenton L. Morgan
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Learning Algorithm ◽  
Characteristic Curve ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Important Indicator ◽  
Unseen Data

Machine-learning algorithms pervade our daily lives. In epidemiology, supervised machine learning has the potential for classification, diagnosis and risk factor identification. Here, we report the use of support vector machine learning to identify the features associated with hock burn on commercial broiler farms, using routinely collected farm management data. These data lend themselves to analysis using machine-learning techniques. Hock burn, dermatitis of the skin over the hock, is an important indicator of broiler health and welfare. Remarkably, this classifier can predict the occurrence of high hock burn prevalence with accuracy of 0.78 on unseen data, as measured by the area under the receiver operating characteristic curve. We also compare the results with those obtained by standard multi-variable logistic regression and suggest that this technique provides new insights into the data. This novel application of a machine-learning algorithm, embedded in poultry management systems could offer significant improvements in broiler health and welfare worldwide.

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