Indoor location recognition using fusion of SVM-based visual classifiers

Multimodal Approaches for Indoor Localization for Ambient Assisted Living in Smart Homes

“…The framework was equipped with the functionality to build the decision tree in the off-line phase and it used the fingerprinting approach for Indoor Localization. Sjoberg et al [11] developed a visual recognition approach using the support vector machine (SVM) classifier. The system consisted of a visual bag-of-words model and other visual features of the environment that were used to train this classifier for Indoor Localization.…”

“…As outlined in Section 2, one the research challenges in this field of Indoor Localization is the need to develop an optimal machine learning model for Indoor Localization systems, Indoor Positioning Systems, and Location-Based Services. In [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25], researchers have used multiple machine learning approaches-Random Forest, Artificial Neural Network, Decision Tree, Support Vector Machine, k-NN, Gradient Boosted Trees, Deep Learning, and Linear Regression. However, none of these works implemented multiple machine learning models to evaluate and compare the associated performance characteristics to deduce the optimal machine learning approach.…”

“…However, none of these works implemented multiple machine learning models to evaluate and compare the associated performance characteristics to deduce the optimal machine learning approach. Due to the differences in the datasets used or the real-time data that was collected, the associated data preprocessing steps that were different, variations in train and test ratio of the data, and several other dissimilar steps that were associated with the developments of each of these machine learning models as presented in [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25], their final performance accuracies cannot be directly compared to deduce the best approach. Thus, analyzing the performance characteristics of multiple machine learning models, developed, implemented, and tested as per the same methodology, to deduce the optimal approach for development of such Indoor Localization systems serves as the main motivation for the work presented in the section.…”

“…These results are shown in Table 7. In this section, we followed the same steps as outlined in Section 4.3 and as per the flowchart shown in Figure 11, to develop, implement, and test this methodology by using all the machine learning methods that have been used by researchers [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25] in this field. These machine learning methods included-Random Forest, Artificial Neural Network, Decision Tree, Support Vector Machine, k-NN, Gradient Boosted Trees, Deep Learning, and Linear Regression.…”

Image pseudo tag generation with Deep Boltzmann machine anc topic-concept similarity map

“…The Support Vector Machine (SVM) has been a great success in many classification tasks in the last decade [100,34,15]. First of all, let we con-…”