Along with the proliferation of mobile devices and wireless signal coverage, indoor localization based on Wi-Fi gets great popularity. Fingerprint based method is the mainstream approach for Wi-Fi indoor localization, for it can achieve high localization performance as long as labeled data are sufficient. However, the number of labeled data is always limited due to the high cost of data acquisi...

Sequential learning is the discipline of machine learning that deals with dependent data such that neighboring labels exhibit some kind of relationship. The paper main contribution is two-fold: first, we generalize the stacked sequential learning, highlighting the key role of neighboring interactions modeling. Second, we propose an effective and efficient way of capturing and exploiting sequent...

This paper proposes a novel method for supervised subspace learning based on Single-hidden Layer Feedforward Neural networks. The proposed method calculates appropriate network target vectors by formulating a Bayesian model exploiting both the labeling information available for the training data and geometric properties of the training data, when represented in the feature space determined by t...

Stochastic gradient descent based algorithms are typically used as the general optimization tools for most deep learning models. A Restricted Boltzmann Machine (RBM) is a probabilistic generative model that can be stacked to construct deep architectures. For RBM with Bernoulli inputs, non-Euclidean algorithm such as stochastic spectral descent (SSD) has been specifically designed to speed up th...

A recent work introduced the concept of deep dictionary learning. The first level is a dictionary learning stage where the inputs are the training data and the outputs are the dictionary and learned coefficients. In subsequent levels of deep dictionary learning, the learned coefficients from the previous level acts as inputs. This is an unsupervised representation learning technique. In this wo...

Deep learning has become a research hotspot in the field of network intrusion detection. In order to further improve detection accuracy and performance, we proposed an model based on improved deep belief (DBN). Traditional neural training methods, like Back Propagation (BP), start train with preset parameters such as randomly initialized weights thresholds, which may bring some issues, e.g., at...

Extreme learning machines (ELMs) are a versatile machine technique that can be seamlessly implemented with optical systems. In short, they described as network of hidden neurons random fixed weights and biases, generate complex behaviour in response to an input. Yet, despite the success physical implementations ELMs, there is still lack fundamental understanding about their implementations. Thi...

Abstract In the present study, we aim to propose an effective and robust ensemble-learning approach with stacked generalization for image segmentation. Initially, input images are processed feature extraction edge detection using Gabor filter Canny algorithms, respectively; our main goal is determine most descriptions. Subsequently, applied stacking technique, which generally built two learning...

In this work, an attempt has been made to analyze human femur radiographic bone images using sharpness features and learning models. The sharpness features are derived for the neck of the femur bone images to characterize the trabecular structure. The significant parameters are found using Independent component analysis (ICA) and Principal Component Analysis (PCA). The first three most signific...