scholarly journals Investigating Learning with Few Shot Data

2020 ◽  
Author(s):  
Stefanie
Keyword(s):  
Learning Strategy ◽  
Teaching Strategy ◽  
Training Sample ◽  
Learning To Teach ◽  
Training Data ◽  
Training Dataset ◽  
Small Data ◽  
Importance Score ◽  
Training Samples ◽  
Teacher Model

In this project, I explore a teaching strategy called learning to teach (L2T) in which a teacher model could provide high-quality training samples to a student model. However, one major problem of L2T is that the teacher model will only select a subset of the training dataset as the final training data for the student. A learning to teach small-data learning strategy (L2TSDL) is proposed to solve this problem. In this strategy, the teacher model will calculate the importance score for every training sample and help student to make use of all training samples.

scholarly journals Learning with small amount of data on various task

2020 ◽  
Author(s):  
Stefanie
Keyword(s):  
Deep Neural Networks ◽  
Learning Strategy ◽  
Teaching Strategy ◽  
Training Sample ◽  
Learning To Teach ◽  
Training Data ◽  
Training Dataset ◽  
Small Data ◽  
Training Samples ◽  
Teacher Model

As a student, I am learning knowledge with the help of teachers and the teacher plays a crucial role in our life. A wonderful instructor is able to teach a student with appropriate teaching materials. Therefore, in this project, I explore a teaching strategy called learning to teach (L2T) in which a teacher model could provide high-quality training samples to a student model. However, one major problem of L2T is that the teacher model will only select a subset of the training dataset as the final training data for the student. Learning to teach small-data learning strategy (L2TSDL) is proposed to solve this problem. In this strategy, the teacher model will calculate the importance score for every training sample and help students to make use of all training samples. To demonstrate the advantage of the proposed approach over L2T, I take the training of different deep neural networks (DNN) on image classification task as an exampleand show that L2TSDL could achieve good performance on both large and small dataset.

scholarly journals AIRBORNE HYPERSPECTRAL REMOTE SENSING FOR IDENTIFICATION GRASSLAND VEGETATION

2015 ◽  
Vol XL-3/W3 ◽  
pp. 427-431 ◽  
Author(s):  
P. Burai ◽  
T. Tomor ◽  
L. Bekő ◽  
B. Deák
Keyword(s):  
Image Classification ◽  
Learning Algorithm ◽  
Training Sample ◽  
Hyperspectral Data ◽  
Training Dataset ◽  
Classification Methods ◽  
Grassland Vegetation ◽  
Training Samples ◽  
Almost All ◽  
Noise Fraction

In our study we classified grassland vegetation types of an alkali landscape (Eastern Hungary), using different image classification methods for hyperspectral data. Our aim was to test the applicability of hyperspectral data in this complex system using various image classification methods. To reach the highest classification accuracy, we compared the performance of traditional image classifiers, machine learning algorithm, feature extraction (MNF-transformation) and various sizes of training dataset. Hyperspectral images were acquired by an AISA EAGLE II hyperspectral sensor of 128 contiguous bands (400–1000 nm), a spectral sampling of 5 nm bandwidth and a ground pixel size of 1 m. We used twenty vegetation classes which were compiled based on the characteristic dominant species, canopy height, and total vegetation cover. Image classification was applied to the original and MNF (minimum noise fraction) transformed dataset using various training sample sizes between 10 and 30 pixels. In the case of the original bands, both SVM and RF classifiers provided high accuracy for almost all classes irrespectively of the number of the training pixels. We found that SVM and RF produced the best accuracy with the first nine MNF transformed bands. Our results suggest that in complex open landscapes, application of SVM can be a feasible solution, as this method provides higher accuracies compared to RF and MLC. SVM was not sensitive for the size of the training samples, which makes it an adequate tool for cases when the available number of training pixels are limited for some classes.

scholarly journals Iterative Metric Learning for Imbalance Data Classification

2018 ◽  
Author(s):  
Nan Wang ◽  
Xibin Zhao ◽  
Yu Jiang ◽  
Yue Gao
Keyword(s):  
Learning Strategy ◽  
State Of The Art ◽  
Metric Learning ◽  
Training Data ◽  
Imbalance Data ◽  
Data Space ◽  
Software Defect ◽  
Training Samples ◽  
Testing Data ◽  
Data Program

In many classification applications, the amount of data from different categories usually vary significantly, such as software defect predication and medical diagnosis. Under such circumstances, it is essential to propose a proper method to solve the imbalance issue among the data. However, most of the existing methods mainly focus on improving the performance of classifiers rather than searching for an appropriate way to find an effective data space for classification. In this paper, we propose a method named Iterative Metric Learning (IML) to explore the correlations among imbalance data and construct an effective data space for classification. Given the imbalance training data, it is important to select a subset of training samples for each testing data. Thus, we aim to find a more stable neighborhood for testing data using the iterative metric learning strategy. To evaluate the effectiveness of the proposed method, we have conducted experiments on two groups of dataset, i.e., the NASA Metrics Data Program (NASA) dataset and UCI Machine Learning Repository (UCI) dataset. Experimental results and comparisons with state-of-the-art methods have exhibited better performance of our proposed method.

scholarly journals Classifiers ensemble training on unbalanced samples in the analysis of the network segments state

2021 ◽  
Vol 16 (93) ◽  
pp. 109-119
Author(s):  
Ilya S. Lebedev ◽  
Keyword(s):  
Imbalanced Data ◽  
Training Sample ◽  
Training Data ◽  
Model Complexity ◽  
Ensemble Averaging ◽  
Destructive Effect ◽  
Security Monitoring ◽  
Training Samples ◽  
The Internet Of Things ◽  
Training Sets

The relevance of the topic considered in the article lies in solving problematic issues of identifying rare events in imbalance conditions in training sets. The purpose of the study is to analyze the capabilities of a classifier’s ensemble trained on different imbalanced data subsets. The features of the heterogeneous segments state analysis of the Internet of Things network infrastructure based on machine learning methods are considered. The prerequisites for the unbalanced data emergence during the training samples formation are indicated. A solution based on the use of a classifier’s ensemble trained on various training samples with classified events imbalance is proposed. The possibility analysis of using unbalanced training sets for a classifier’s ensemble averaging of errors occurs due to the collective voting procedure, is given. An experiment was carried out using weak classifying algorithms. The estimation of features values distributions in test and training subsets is carried out. The classification results are obtained for the ensemble and each classifier separately. An imbalance is investigated consists in the events number ratios violation a certain type within one class in the training data subsets. The data absence in the training sample leads to an increase in the scatter effect responses is averaged by an increase in the model complexity including various classifying algorithms in its composition. The proposed approach can be applied in information security monitoring systems. A proposed solution feature is the ability to scale and combine it by adding new classifying algorithms. In the future, it is possible to make changes during operation to the classification algorithms composition, it makes possible to increase the indicators of the identifying accuracy of a potential destructive effect.

scholarly journals Abstraction and Association: Cross-Modal Retrieval Based on Consistency between Semantic Structures

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Qibin Zheng ◽  
Xiaoguang Ren ◽  
Yi Liu ◽  
Wei Qin
Keyword(s):  
Learning Strategy ◽  
Reference Points ◽  
Training Data ◽  
Semantic Structure ◽  
Retrieval Method ◽  
Multiple Modalities ◽  
Training Samples ◽  
High Level ◽  
Active Learning Strategy ◽  
The Relationship

Cross-modal retrieval aims to find relevant data of different modalities, such as images and text. In order to bridge the modality gap, most existing methods require a lot of coupled sample pairs as training data. To reduce the demands for training data, we propose a cross-modal retrieval framework that utilizes both coupled and uncoupled samples. The framework consists of two parts: Abstraction that aims to provide high-level single-modal representations with uncoupled samples; then, Association links different modalities through a few coupled training samples. Moreover, under this framework, we implement a cross-modal retrieval method based on the consistency between the semantic structure of multiple modalities. First, both images and text are represented with the semantic structure-based representation, which represents each sample as its similarity from the reference points that are generated from single-modal clustering. Then, the reference points of different modalities are aligned through an active learning strategy. Finally, the cross-modal similarity can be measured with the consistency between the semantic structures. The experiment results demonstrate that given proper abstraction of single-modal data, the relationship between different modalities can be simplified, and even limited coupled cross-modal training data are sufficient for satisfactory retrieval accuracy.

scholarly journals MODIFIED CORRELATION WEIGHT K-NEAREST NEIGHBOR CLASSIFIER USING TRAINING DATASET CLEANING METHOD

2021 ◽  
Vol 32 (2) ◽  
pp. 20-25
Author(s):  
Efraim Kurniawan Dairo Kette
Keyword(s):  
Nearest Neighbor ◽  
Training Sample ◽  
Classification Performance ◽  
Training Data ◽  
Training Dataset ◽  
Classification Problems ◽  
K Nearest Neighbor ◽  
Neighborhood Structure ◽  
Data Set ◽  
Sample Data

In pattern recognition, the k-Nearest Neighbor (kNN) algorithm is the simplest non-parametric algorithm. Due to its simplicity, the model cases and the quality of the training data itself usually influence kNN algorithm classification performance. Therefore, this article proposes a sparse correlation weight model, combined with the Training Data Set Cleaning (TDC) method by Classification Ability Ranking (CAR) called the CAR classification method based on Coefficient-Weighted kNN (CAR-CWKNN) to improve kNN classifier performance. Correlation weight in Sparse Representation (SR) has been proven can increase classification accuracy. The SR can show the 'neighborhood' structure of the data, which is why it is very suitable for classification based on the Nearest Neighbor. The Classification Ability (CA) function is applied to classify the best training sample data based on rank in the cleaning stage. The Leave One Out (LV1) concept in the CA works by cleaning data that is considered likely to have the wrong classification results from the original training data, thereby reducing the influence of the training sample data quality on the kNN classification performance. The results of experiments with four public UCI data sets related to classification problems show that the CAR-CWKNN method provides better performance in terms of accuracy.

scholarly journals DEEP MULTI-TASK LEARNING FOR TREE GENERA CLASSIFICATION

2018 ◽  
Vol IV-2 ◽  
pp. 153-159 ◽  
Author(s):  
C. Ko ◽  
J. Kang ◽  
G. Sohn
Keyword(s):  
Deep Learning ◽  
Classification Accuracy ◽  
Network Performance ◽  
Training Sample ◽  
Training Data ◽  
Training Dataset ◽  
Main Task ◽  
Data Generation ◽  
Multiple View

The goal for our paper is to classify tree genera using airborne Light Detection and Ranging (LiDAR) data with Convolution Neural Network (CNN) &amp;ndash; Multi-task Network (MTN) implementation. Unlike Single-task Network (STN) where only one task is assigned to the learning outcome, MTN is a deep learning architect for learning a main task (classification of tree genera) with other tasks (in our study, classification of coniferous and deciduous) simultaneously, with shared classification features. The main contribution of this paper is to improve classification accuracy from CNN-STN to CNN-MTN. This is achieved by introducing a concurrence loss (<i>L</i><sub>cd</sub>) to the designed MTN. This term regulates the overall network performance by minimizing the inconsistencies between the two tasks. Results show that we can increase the classification accuracy from 88.7&amp;thinsp;% to 91.0&amp;thinsp;% (from STN to MTN). The second goal of this paper is to solve the problem of small training sample size by multiple-view data generation. The motivation of this goal is to address one of the most common problems in implementing deep learning architecture, the insufficient number of training data. We address this problem by simulating training dataset with multiple-view approach. The promising results from this paper are providing a basis for classifying a larger number of dataset and number of classes in the future.

scholarly journals Discriminative Structure Learning of Bayesian Network Classifiers from Training Dataset and Testing Instance

Entropy ◽  
2019 ◽  
Vol 21 (5) ◽  
pp. 489 ◽  
Author(s):  
Limin Wang ◽  
Yang Liu ◽  
Musa Mammadov ◽  
Minghui Sun ◽  
Sikai Qi
Keyword(s):  
Bayesian Network ◽  
Learning Strategy ◽  
Structure Learning ◽  
Naive Bayes ◽  
Search Space ◽  
Naïve Bayes ◽  
Bayesian Classifier ◽  
Training Data ◽  
Training Dataset ◽  
Bayesian Network Classifiers

Over recent decades, the rapid growth in data makes ever more urgent the quest for highly scalable Bayesian networks that have better classification performance and expressivity (that is, capacity to respectively describe dependence relationships between attributes in different situations). To reduce the search space of possible attribute orders, k-dependence Bayesian classifier (KDB) simply applies mutual information to sort attributes. This sorting strategy is very efficient but it neglects the conditional dependencies between attributes and is sub-optimal. In this paper, we propose a novel sorting strategy and extend KDB from a single restricted network to unrestricted ensemble networks, i.e., unrestricted Bayesian classifier (UKDB), in terms of Markov blanket analysis and target learning. Target learning is a framework that takes each unlabeled testing instance P as a target and builds a specific Bayesian model Bayesian network classifiers (BNC) P to complement BNC T learned from training data T . UKDB respectively introduced UKDB P and UKDB T to flexibly describe the change in dependence relationships for different testing instances and the robust dependence relationships implicated in training data. They both use UKDB as the base classifier by applying the same learning strategy while modeling different parts of the data space, thus they are complementary in nature. The extensive experimental results on the Wisconsin breast cancer database for case study and other 10 datasets by involving classifiers with different structure complexities, such as Naive Bayes (0-dependence), Tree augmented Naive Bayes (1-dependence) and KDB (arbitrary k-dependence), prove the effectiveness and robustness of the proposed approach.

Strut Diameter Uncertainty Prediction by Deep Neural Network for Additively Manufactured Lattice Structures

2021 ◽  
pp. 1-58
Author(s):  
Recep M. Gorguluarslan ◽  
Gorkem Can Ates ◽  
Olgun Utku Gungor ◽  
Yusuf Yamaner
Keyword(s):  
Extrusion Process ◽  
Training Data ◽  
Training Dataset ◽  
Small Data ◽  
Lattice Structures ◽  
Statistical Parameters ◽  
Data Set ◽  
Random Field Theory ◽  
Geometric Uncertainties ◽  
Upscaling Technique

Abstract Additive manufacturing (AM) introduces geometric uncertainties on the fabricated strut members of lattice structures. These uncertainties result in deviations between the modeled and fabricated geometries of struts. The use of deep neural networks (DNNs) to accurately predict the statistical parameters of the effective strut diameters to account for the AM-introduced geometric uncertainties with a small training dataset for constant process parameters is studied in this research. For the training data, struts with certain angle and diameter values are fabricated by the material extrusion process. The geometric uncertainties are quantified using the random field theory based on the spatial strut radius measurements obtained from the microscope images of the fabricated struts. The uncertainties are propagated to the effective diameters of the struts using a stochastic upscaling technique. The relationship between the modeled strut diameter and the characterized statistical parameters of the effective diameters are used as the training data to establish a DNN model. The validation results show that the DNN model can predict the statistical parameters of the effective diameters of the struts modeled with angle and diameters different from the ones used in the training data with good accuracy even if the training data set is small. Developing such a DNN model with a small data will allow designers to use the fabricated results in the design optimization processes without requiring additional experimentations.

scholarly journals Few-Shot Website Fingerprinting Attack with Data Augmentation

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Mantun Chen ◽  
Yongjun Wang ◽  
Zhiquan Qin ◽  
Xiatian Zhu
Keyword(s):  
Deep Learning ◽  
Data Augmentation ◽  
State Of The Art ◽  
Training Data ◽  
Training Dataset ◽  
Feature Representations ◽  
Open World ◽  
Closed World ◽  
Training Samples ◽  
Unrealistic Assumption

This work introduces a novel data augmentation method for few-shot website fingerprinting (WF) attack where only a handful of training samples per website are available for deep learning model optimization. Moving beyond earlier WF methods relying on manually-engineered feature representations, more advanced deep learning alternatives demonstrate that learning feature representations automatically from training data is superior. Nonetheless, this advantage is subject to an unrealistic assumption that there exist many training samples per website, which otherwise will disappear. To address this, we introduce a model-agnostic, efficient, and harmonious data augmentation (HDA) method that can improve deep WF attacking methods significantly. HDA involves both intrasample and intersample data transformations that can be used in a harmonious manner to expand a tiny training dataset to an arbitrarily large collection, therefore effectively and explicitly addressing the intrinsic data scarcity problem. We conducted expensive experiments to validate our HDA for boosting state-of-the-art deep learning WF attack models in both closed-world and open-world attacking scenarios, at absence and presence of strong defense. For instance, in the more challenging and realistic evaluation scenario with WTF-PAD-based defense, our HDA method surpasses the previous state-of-the-art results by nearly 3% in classification accuracy in the 20-shot learning case. An earlier version of this work Chen et al. (2021) has been presented as preprint in ArXiv (https://arxiv.org/abs/2101.10063).

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