Efficient Learning Method for Human Detection based on Automatic Generation of Training Samples with the Negative-Bag MILBoost

2014 ◽  
Vol 134 (3) ◽  
pp. 450-458
Author(s):  
Masamitsu Tsuchiya ◽  
Yuji Yamauchi ◽  
Hironobu Fujiyoshi
Keyword(s):  
Automatic Generation ◽  
Human Detection ◽  
Learning Method ◽  
Training Samples ◽  
Efficient Learning

scholarly journals Siamese Reconstruction Network: Accurate Image Reconstruction from Human Brain Activity by Learning to Compare

2019 ◽  
Vol 9 (22) ◽  
pp. 4749
Author(s):  
Lingyun Jiang ◽  
Kai Qiao ◽  
Linyuan Wang ◽  
Chi Zhang ◽  
Jian Chen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Human Brain ◽  
Brain Activity ◽  
Feature Space ◽  
Training Data ◽  
Reconstruction Method ◽  
Learning Method ◽  
Training Samples ◽  
Visual Reconstruction ◽  
Relationship Of

Decoding human brain activities, especially reconstructing human visual stimuli via functional magnetic resonance imaging (fMRI), has gained increasing attention in recent years. However, the high dimensionality and small quantity of fMRI data impose restrictions on satisfactory reconstruction, especially for the reconstruction method with deep learning requiring huge amounts of labelled samples. When compared with the deep learning method, humans can recognize a new image because our human visual system is naturally capable of extracting features from any object and comparing them. Inspired by this visual mechanism, we introduced the mechanism of comparison into deep learning method to realize better visual reconstruction by making full use of each sample and the relationship of the sample pair by learning to compare. In this way, we proposed a Siamese reconstruction network (SRN) method. By using the SRN, we improved upon the satisfying results on two fMRI recording datasets, providing 72.5% accuracy on the digit dataset and 44.6% accuracy on the character dataset. Essentially, this manner can increase the training data about from n samples to 2n sample pairs, which takes full advantage of the limited quantity of training samples. The SRN learns to converge sample pairs of the same class or disperse sample pairs of different class in feature space.

scholarly journals PENGEMBANGAN MODEL PEMBELAJARAN PEMERANAN BERORIENTASI TEKNIK CS PADA TEATER TRADISI

2018 ◽  
Vol 14 (25) ◽  
pp. 49-58
Author(s):  
Moh mujib Alfirdaus
Keyword(s):  
Learning Process ◽  
Learning Model ◽  
Learning Method ◽  
Academic Field ◽  
Efficient Learning ◽  
Rule Out

This learning model is the author's attempt to develop acting method in traditional theater through the Stanislavski's technique, although the need for theater tradition performances and theater conventional realism performance is different. During this time the method of play in the theater tradition is still spontaneous, but the method of acting on the theater tradition must be measurable and can be studied in the academic field, hence, the author develops acting methods based on Stanislavski's technique as a reference in learning. An actor is a student for nature and pupil for anyone as long as the knowledge he earned is useful to develops his acting creativity. Therefore this Stanislavski's method becomes very influential to train the actor's intelligence, despite his need for traditional theater. Why is Stanislavski's method becoming important to be learned by actor candidate ?. Because the analysis used by Stanislavski's method is still very logical and reasonable, it did not rule out the effects of int elligence for anyone who  applied  it.  This  circumstance emphasizing  the  importance of Developing  Stanilavski's technique-oriented Learning Model on Traditional Theater. In order for candidates who will perform for traditional and modern show, are expected to be ready with all the acting devices to employ. Therefore, this learning method need to be applied, especially in STKW Surabaya. The purpose of this research is developing a learning model for acting in the theater tradition. This research carried out  by producing several outcome. First, a handbook of Stanislavski's method learning model for student. Second, a lecturer's handbook for an effective and efficient learning process

scholarly journals BEAN: Interpretable and Efficient Learning With Biologically-Enhanced Artificial Neuronal Assembly Regularization

2021 ◽  
Vol 15 ◽  
Author(s):  
Yuyang Gao ◽  
Giorgio A. Ascoli ◽  
Liang Zhao
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Model Performance ◽  
Semantic Content ◽  
Population Coding ◽  
Crucial Issue ◽  
Synaptic Interactions ◽  
Training Samples ◽  
Efficient Learning ◽  
Neuronal Assembly

Deep neural networks (DNNs) are known for extracting useful information from large amounts of data. However, the representations learned in DNNs are typically hard to interpret, especially in dense layers. One crucial issue of the classical DNN model such as multilayer perceptron (MLP) is that neurons in the same layer of DNNs are conditionally independent of each other, which makes co-training and emergence of higher modularity difficult. In contrast to DNNs, biological neurons in mammalian brains display substantial dependency patterns. Specifically, biological neural networks encode representations by so-called neuronal assemblies: groups of neurons interconnected by strong synaptic interactions and sharing joint semantic content. The resulting population coding is essential for human cognitive and mnemonic processes. Here, we propose a novel Biologically Enhanced Artificial Neuronal assembly (BEAN) regularization1 to model neuronal correlations and dependencies, inspired by cell assembly theory from neuroscience. Experimental results show that BEAN enables the formation of interpretable neuronal functional clusters and consequently promotes a sparse, memory/computation-efficient network without loss of model performance. Moreover, our few-shot learning experiments demonstrate that BEAN could also enhance the generalizability of the model when training samples are extremely limited.

scholarly journals Pengembangan Keterampilan Berbicara dengan Metode Role Playing pada Mata Pelajaran Bahasa Arab

2017 ◽  
Vol 5 (1) ◽  
pp. 1-10
Author(s):  
Nurul Hidayah
Keyword(s):  
Learning Process ◽  
Role Playing ◽  
Student Interest ◽  
Student Life ◽  
Learning Method ◽  
Human Relationships ◽  
Efficient Learning ◽  
Language Mastery

There are some problems in the learning process, for example: Students look less interested and motivated to follow the lesson, the teacher gets difficulties in arising student interest, and most students have difficulty and fear to express opinions and perform in front of the class. This condition causes students’ achievement to decrease. Such condition happen in the Arabic learning process in Madrasah Aliyah and to handle it, there should be an appropriate learning method to build effective and efficient learning. A method of learning that can be developed is role playing. Role playing is a method directed at solving problems related to human relationships, especially those involving student life in schools. Role playing can also train good and correct language mastery and strengthening competence in creative and innovative language and make students more mature in learning Arabic and behaving in their environment.

Human Detection in Static Images

2008 ◽  
pp. 227-243 ◽  
Author(s):  
Hui-Xing Jia ◽  
Yu-Jin Zhang
Keyword(s):  
Detection System ◽  
Content Management ◽  
Human Detection ◽  
Classifier Design ◽  
Feature Sets ◽  
Training Samples ◽  
Driving Assistance ◽  
Pros And Cons ◽  
Static Images ◽  
Assistance Systems

Human detection is the first step for a number of applications such as smart video surveillance, driving assistance systems, and intelligent digital content management. It’s a challenging problem due to the variance of illumination, color, scale, pose, and so forth. This chapter reviews various aspects of human detection in static images and focuses on learning-based methods that build classifiers using training samples. There are usually three modules for these methods: feature extraction, classifier design, and merge of overlapping detections. The chapter reviews most existing methods for each module and analyzes their respective pros and cons. The contribution includes two aspects: first, the performance of existing feature sets on human detection are compared; second, a fast human detection system based on histogram of oriented gradients features and cascaded AdaBoost classifier is proposed. This chapter should be useful for both algorithm researchers and system designers in the computer vision and pattern recognition community.

scholarly journals An Arc Fault Detection Method Based on Multidictionary Learning

2018 ◽  
Vol 2018 ◽  
pp. 1-8
Author(s):  
Na Qu ◽  
Jianhui Wang ◽  
Jinhai Liu ◽  
Zhi Wang
Keyword(s):  
Fault Detection ◽  
Sparse Representation ◽  
Dictionary Learning ◽  
Detection Method ◽  
Amplitude Spectrum ◽  
Learning Method ◽  
Test Results ◽  
Training Samples ◽  
Simulation Results ◽  
Normal Work

This paper uses the dictionary learning of sparse representation algorithm to detect the arc fault. Six kinds of characteristics, that is, the normalized amplitudes of 0Hz, 50Hz, 100Hz, 150Hz, 200Hz, and 250Hz in the current amplitude spectrum, are used as inputs. The output is normal work or arc fault. Increasing the number of training samples can improve the accuracy of the tests. But if the training samples are too many, it is difficult to be expressed by single dictionary. This paper designs a multidictionary learning method to solve the problem. Firstly, n training samples are selected to form s overcomplete dictionaries. Then a dictionary library consisting of s dictionaries is constructed. Secondly, t (t≤s) dictionaries are randomly selected from the dictionary library to judge the test results, respectively. Finally, the final detest result is obtained through the maximum number of votes, that is, the modality with the most votes is the detest result. Simulation results show that the accuracy of detection can be improved.

An Efficient Learning Method for Sound Classification using Transfer Learning for Hammering Test

2020 ◽  
Author(s):  
Tsubasa Fukumura ◽  
Hayato Aratame ◽  
Atsushi Ito ◽  
Masafumi Koike ◽  
Katsuhiko Hibino ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Learning Method ◽  
Sound Classification ◽  
Efficient Learning

Automatic Generation of 3D Statistical Shape Models with Optimal Landmark Distributions

2007 ◽  
Vol 46 (03) ◽  
pp. 275-281 ◽  
Author(s):  
I. Wolf ◽  
H.-P. Meinzer ◽  
T. Heimann
Keyword(s):  
Complex Geometry ◽  
Minimum Description Length ◽  
Standard Technique ◽  
Automatic Generation ◽  
Model Construction ◽  
Model Quality ◽  
Training Samples ◽  
Statistical Shape ◽  
Evaluation Metric

Summary Objectives: To point out the problem of non-uniform landmark placement in statistical shape modeling, to present an improved method for generating landmarks in the 3D case and to propose an unbiased evaluation metric to determine model quality. Methods: Our approach minimizes a cost function based on the minimum description length (MDL) of the shape model to optimize landmark correspondences over the training set. In addition to the standard technique, we employ an extended remeshing method to change the landmark distribution without losing correspondences, thus ensuring a uniform distribution over all training samples. To breakthe dependency of the established evaluation measures generalization and specificity from the landmark distribution, we change the internal metric from landmark distance to volumetric overlap. Results: Redistributing landmarks to an equally spaced distribution during the model construction phase improves the quality of the resulting models significantly if the shapes feature prominent bulges or other complex geometry. Conclusions: The distribution of landmarks on the training shapes is – beyond the correspondence issue – a crucial point in model construction.

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