scholarly journals CSI Amplitude Fingerprinting for Indoor Localization with Dictionary Learning

Entropy ◽  
2021 ◽  
Vol 23 (9) ◽  
pp. 1164
Author(s):  
Wen Liu ◽  
Xu Wang ◽  
Zhongliang Deng
Keyword(s):  
Dictionary Learning ◽  
Indoor Localization ◽  
Learning Algorithm ◽  
Indoor Positioning ◽  
Reconstruction Error ◽  
Test Point ◽  
Training Data ◽  
Support Vector ◽  
Location Services ◽  
Indoor Scenes

With the rapid growth of the demand for location services in the indoor environment, fingerprint-based indoor positioning has attracted widespread attention due to its high-precision characteristics. This paper proposes a double-layer dictionary learning algorithm based on channel state information (DDLC). The DDLC system includes two stages. In the offline training stage, a two-layer dictionary learning architecture is constructed for the complex conditions of indoor scenes. In the first layer, for the input training data of different regions, multiple sub-dictionaries are generated corresponding to learning, and non-coherent promotion items are added to emphasize the discrimination between sparse coding in different regions. The second-level dictionary learning introduces support vector discriminant items for the fingerprint points inside each region, and uses Max-margin to distinguish different fingerprint points. In the online positioning stage, we first determine the area of the test point based on the reconstruction error, and then use the support vector discriminator to complete the fingerprint matching work. In this experiment, we selected two representative indoor positioning environments, and compared the DDLC with several existing indoor positioning methods. The results show that DDLC can effectively reduce positioning errors, and because the dictionary itself is easy to maintain and update, the characteristic of strong anti-noise ability can be better used in CSI indoor positioning work.

Adapting SVM for data sparseness and imbalance: a case study in information extraction

2009 ◽  
Vol 15 (2) ◽  
pp. 241-271 ◽  
Author(s):  
YAOYONG LI ◽  
KALINA BONTCHEVA ◽  
HAMISH CUNNINGHAM
Keyword(s):  
Active Learning ◽  
Language Learning ◽  
Information Extraction ◽  
Language Processing ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Support Vector ◽  
Passive Learning ◽  
Wide Range

AbstractSupport Vector Machines (SVM) have been used successfully in many Natural Language Processing (NLP) tasks. The novel contribution of this paper is in investigating two techniques for making SVM more suitable for language learning tasks. Firstly, we propose an SVM with uneven margins (SVMUM) model to deal with the problem of imbalanced training data. Secondly, SVM active learning is employed in order to alleviate the difficulty in obtaining labelled training data. The algorithms are presented and evaluated on several Information Extraction (IE) tasks, where they achieved better performance than the standard SVM and the SVM with passive learning, respectively. Moreover, by combining SVMUM with the active learning algorithm, we achieve the best reported results on the seminars and jobs corpora, which are benchmark data sets used for evaluation and comparison of machine learning algorithms for IE. In addition, we also evaluate the token based classification framework for IE with three different entity tagging schemes. In comparison to previous methods dealing with the same problems, our methods are both effective and efficient, which are valuable features for real-world applications. Due to the similarity in the formulation of the learning problem for IE and for other NLP tasks, the two techniques are likely to be beneficial in a wide range of applications1.

scholarly journals Time-Series Laplacian Semi-Supervised Learning for Indoor Localization †

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3867 ◽  
Author(s):  
Jaehyun Yoo
Keyword(s):  
Time Series ◽  
Supervised Learning ◽  
Indoor Localization ◽  
Learning Algorithm ◽  
Unlabeled Data ◽  
Training Data ◽  
Practical Implementation ◽  
Additional Information ◽  
Localization Scheme ◽  
The Impact

Machine learning-based indoor localization used to suffer from the collection, construction, and maintenance of labeled training databases for practical implementation. Semi-supervised learning methods have been developed as efficient indoor localization methods to reduce use of labeled training data. To boost the efficiency and the accuracy of indoor localization, this paper proposes a new time-series semi-supervised learning algorithm. The key aspect of the developed method, which distinguishes it from conventional semi-supervised algorithms, is the use of unlabeled data. The learning algorithm finds spatio-temporal relationships in the unlabeled data, and pseudolabels are generated to compensate for the lack of labeled training data. In the next step, another balancing-optimization learning algorithm learns a positioning model. The proposed method is evaluated for estimating the location of a smartphone user by using a Wi-Fi received signal strength indicator (RSSI) measurement. The experimental results show that the developed learning algorithm outperforms some existing semi-supervised algorithms according to the variation of the number of training data and access points. Also, the proposed method is discussed in terms of why it gives better performance, by the analysis of the impact of the learning parameters. Moreover, the extended localization scheme in conjunction with a particle filter is executed to include additional information, such as a floor plan.

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.

scholarly journals Extraction of Sea Ice Cover by Sentinel-1 SAR Based on SVM with Unsupervised Generation of Training Data

2020 ◽  
Author(s):  
Xiaoming Li ◽  
Yan Sun ◽  
Qiang Zhang
Keyword(s):  
Machine Learning ◽  
Sea Ice ◽  
Learning Algorithm ◽  
Texture Features ◽  
Open Water ◽  
Ice Cover ◽  
Training Data ◽  
Support Vector ◽  
Training Samples

In this paper, we focus on developing a novel method to extract sea ice cover (i.e., discrimination/classification of sea ice and open water) using Sentinel-1 (S1) cross-polarization (vertical-horizontal, VH or horizontal-vertical, HV) data in extra wide (EW) swath mode based on the machine learning algorithm support vector machine (SVM). The classification basis includes the S1 radar backscatter coefficients and texture features that are calculated from S1 data using the gray level co-occurrence matrix (GLCM). Different from previous methods where appropriate samples are manually selected to train the SVM to classify sea ice and open water, we proposed a method of unsupervised generation of the training samples based on two GLCM texture features, i.e. entropy and homogeneity, that have contrasting characteristics on sea ice and open water. We eliminate the most uncertainty of selecting training samples in machine learning and achieve automatic classification of sea ice and open water by using S1 EW data. The comparison shows good agreement between the SAR-derived sea ice cover using the proposed method and a visual inspection, of which the accuracy reaches approximately 90% - 95% based on a few cases. Besides this, compared with the analyzed sea ice cover data Ice Mapping System (IMS) based on 728 S1 EW images, the accuracy of extracted sea ice cover by using S1 data is more than 80%.

Application of Support Vector Machine in Determination of Real Estate Price

2012 ◽  
Vol 461 ◽  
pp. 818-821
Author(s):  
Shi Hu Zhang
Keyword(s):  
Support Vector Machine ◽  
Real Estate ◽  
Learning Algorithm ◽  
Predictive Ability ◽  
Training Data ◽  
Small Samples ◽  
Support Vector ◽  
Data Set ◽  
Real Estate Price

The problem of real estate prices are the current focus of the community's concern. Support Vector Machine is a new machine learning algorithm, as its excellent performance of the study, and in small samples to identify many ways, and so has its unique advantages, is now used in many areas. Determination of real estate price is a complicated problem due to its non-linearity and the small quantity of training data. In this study, support vector machine (SVM) is proposed to forecast the price of real estate price in China. The experimental results indicate that the SVM method can achieve greater accuracy than grey model, artificial neural network under the circumstance of small training data. It was also found that the predictive ability of the SVM outperformed those of some traditional pattern recognition methods for the data set used here.

A Machine Learning Algorithm to Predict Hyperglycemic Cases Induced by PD-1/PD-L1 Inhibitors in the Real World (Preprint)

2021 ◽  
Author(s):  
Jincheng Yang
Keyword(s):  
Machine Learning ◽  
Adverse Reaction ◽  
Prediction Model ◽  
Clinical Decision Making ◽  
Learning Algorithm ◽  
Adverse Event Reporting System ◽  
Training Data ◽  
Support Vector ◽  
Machine Learning Algorithm ◽  
Total Drug

BACKGROUND Diabetes mellitus and cancer are amongst the leading causes of deaths worldwide; hyperglycemia plays a major contributory role in neoplastic transformation risk. Support Vector Machine (SVM) is a type of supervised learning method which analyzes data and recognizes patterns, mainly used for statistical classification and regression. OBJECTIVE From reported adverse events of PD-1 or PD-L1 (programmed death 1 or ligand 1) inhibitors in post-marketing monitoring, we aimed to construct an effective machine learning algorithm to predict the probability of hyperglycemic adverse reaction from PD-1/PD-L1 inhibitors treated patients efficiently and rapidly. METHODS Raw data was downloaded from US Food and Drug Administration Adverse Event Reporting System (FDA FAERS). Signal of relationship between drug and adverse reaction based on disproportionality analysis and Bayesian analysis. A multivariate pattern classification of SVM was used to construct classifier to separate adverse hyperglycemic reaction patients. A 10-fold-3-time cross validation for model setup within training data (80% data) output best parameter values in SVM within R software. The model was validated in each testing data (20% data) and two total drug data, with exactly predictor parameter variables: gamma and nu. RESULTS Total 95918 case files were downloaded from 7 relevant drugs (cemiplimab, avelumab, durvalumab, atezolizumab, pembrolizumab, ipilimumab, nivolumab). The number-type/number-optimization method was selected to optimize model. Both gamma and nu values correlated with case number showed high adjusted r2 in curve regressions (both r2 >0.95). Indexes of accuracy, F1 score, kappa and sensitivity were greatly improved from the prediction model in training data and two total drug data. CONCLUSIONS The SVM prediction model established here can non-invasively and precisely predict occurrence of hyperglycemic adverse drug reaction (ADR) in PD-1/PD-L1 inhibitors treated patients. Such information is vital to overcome ADR and to improve outcomes by distinguish high hyperglycemia-risk patients, and this machine learning algorithm can eventually add value onto clinical decision making. CLINICALTRIAL N/A

A Machine Learning Algorithm to Predict Hyperglycemic Cases Induced by PD-1/PD-L1 Inhibitors in the Real World

2020 ◽  
Author(s):  
Jincheng Yang ◽  
Weilong Lin ◽  
Liming Shi ◽  
Ming Deng ◽  
Wenjing Yang
Keyword(s):  
Machine Learning ◽  
Adverse Reaction ◽  
Prediction Model ◽  
Clinical Decision Making ◽  
Learning Algorithm ◽  
Adverse Event Reporting System ◽  
Training Data ◽  
Support Vector ◽  
Machine Learning Algorithm ◽  
Total Drug

Abstract Background: Diabetes mellitus and cancer are amongst the leading causes of deaths worldwide; hyperglycemia plays a major contributory role in neoplastic transformation risk. From reported adverse events of PD-1 or PD-L1 (programmed death 1 or ligand 1) inhibitors in post-marketing monitoring, we aimed to construct an effective machine learning algorithm to predict the probability of hyperglycemic adverse reaction from PD-1/PD-L1 inhibitors treated patients efficiently and rapidly. Methods: Raw data was downloaded from US Food and Drug Administration Adverse Event Reporting System (FDA FAERS). Signal of relationship between drug and adverse reaction based on disproportionality analysis and Bayesian analysis. A multivariate pattern classification of Support Vector Machine (SVM) was used to construct classifier to separate adverse hyperglycemic reaction patients. A 10-fold-3-time cross validation for model setup within training data (80% data) output best parameter values in SVM within R software. The model was validated in each testing data (20% data) and two total drug data, with exactly predictor parameter variables: gamma and nu. Results: Total 95918 case files were downloaded from 7 relevant drugs (cemiplimab, avelumab, durvalumab, atezolizumab, pembrolizumab, ipilimumab, nivolumab). The number-type/number-optimization method was selected to optimize model. Both gamma and nu values correlated with case number showed high adjusted r2 in curve regressions (both r2 >0.95). Indexes of accuracy, F1 score, kappa and sensitivity were greatly improved from the prediction model in training data and two total drug data. Conclusions: The SVM prediction model established here can non-invasively and precisely predict occurrence of hyperglycemic adverse drug reaction (ADR) in PD-1/PD-L1 inhibitors treated patients. Such information is vital to overcome ADR and to improve outcomes by distinguish high hyperglycemia-risk patients, and this machine learning algorithm can eventually add value onto clinical decision making.

scholarly journals Estimating the Support of a High-Dimensional Distribution

2001 ◽  
Vol 13 (7) ◽  
pp. 1443-1471 ◽  
Author(s):  
Bernhard Schölkopf ◽  
John C. Platt ◽  
John Shawe-Taylor ◽  
Alex J. Smola ◽  
Robert C. Williamson
Keyword(s):  
A Priori ◽  
Natural Extension ◽  
Feature Space ◽  
Weight Vector ◽  
Test Point ◽  
Training Data ◽  
Sequential Optimization ◽  
Support Vector ◽  
Small Subset ◽  
Data Set

Suppose you are given some data set drawn from an underlying probability distribution P and you want to estimate a “simple” subset S of input space such that the probability that a test point drawn from P lies outside of S equals some a priori specified value between 0 and 1. We propose a method to approach this problem by trying to estimate a function f that is positive on S and negative on the complement. The functional form of f is given by a kernel expansion in terms of a potentially small subset of the training data; it is regularized by controlling the length of the weight vector in an associated feature space. The expansion coefficients are found by solving a quadratic programming problem, which we do by carrying out sequential optimization over pairs of input patterns. We also provide a theoretical analysis of the statistical performance of our algorithm. The algorithm is a natural extension of the support vector algorithm to the case of unlabeled data.

An Online Prediction Model for BFG Output in Steel Industry

2012 ◽  
Vol 542-543 ◽  
pp. 507-512 ◽  
Author(s):  
Xiaoping Zhang ◽  
Jun Zhao
Keyword(s):  
Prediction Model ◽  
Steel Industry ◽  
Learning Algorithm ◽  
Difficult Problem ◽  
Parameters Optimization ◽  
Training Data ◽  
Support Vector ◽  
Mode Decomposition ◽  
Online Learning Algorithm ◽  
Gas Balance

The output prediction of blast furnace gas (BFG), influenced by many complex production factors, is a very important and difficult problem concerning the byproduct gas balance in steel industry. A new online least squares support vector machine (LSSVM) prediction model is proposed in this paper, in which the training data is filtered by an improved empirical mode decomposition threshold filtering (IEMDTF). The model is solved and optimized by an online learning algorithm and an online bayesian parameters optimization, respectively. The experimental results using practical BFG output data from BaoSteel Co. Ltd., China show the proposed model is effective and enable to offer reasonable gas balance scheduling for operators.

scholarly journals An RSS Transform—Based WKNN for Indoor Positioning

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5685
Author(s):  
Rong Zhou ◽  
Yexi Yang ◽  
Puchun Chen
Keyword(s):  
Real Time ◽  
Nearest Neighbor ◽  
Environmental Changes ◽  
Indoor Positioning ◽  
Test Point ◽  
Gaussian Kernel ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Positioning Accuracy ◽  
Time Performance

An RSS transform–based weighted k-nearest neighbor (WKNN) indoor positioning algorithm, Q-WKNN, is proposed to improve the positioning accuracy and real-time performance of Wi-Fi fingerprint–based indoor positioning. To smooth the RSS fluctuation difference caused by acquisition equipment, time, and environment changes, base Q is introduced in Q-WKNN to transform RSS to Q-based RSS, based on the relationship between the received signal strength (RSS) and physical distance. Analysis of the effective range of base Q indicates that Q-WKNN is more suitable for regions with noticeable environmental changes and fixed access points (APs). To reduce the positioning time, APs are selected to form a Q-WKNN similarity matrix. Adaptive K is applied to estimate the test point (TP) position. Commonly used indoor positioning algorithms are compared to Q-WKNN on Zenodo and underground parking databases. Results show that Q-WKNN has better positioning accuracy and real-time performance than WKNN, modified-WKNN (M-WKNN), Gaussian kernel (GK), and least squares-support vector machine (LS-SVM) algorithms.

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