An effective way to improve the performance of deep neural networks in most computer vision tasks is quantity labeled data and quality labels. However, analysis processing medical images, high-quality annotation depends on experience professional knowledge experts, which makes it very difficult obtain a large number annotations. Therefore, we propose new semi-supervised framework for image clas...

-A scene independent linear transformation called – multitemporal Kauth-Thomas transformation (MKT) was implemented in this land surface change monitoring due to mining activities. 5-fold cross validation results indicated the high feasibility of using the MKT method with a computer-based hybrid unsupervised and supervised classification approach for mining monitoring.

    Drug resistance enables cancer cells to break away from cytotoxic effect of anticancer drugs. Identification of resistant phenotype is very important because it can lead to effective treatment plan. There is an interest in developing classifying models of resistance phenotype based on the multivariate data. We have investigated a vibrational spectroscopic approach in order to characterize a...

This paper presents a review of different classification techniques used to recognize human activities from wearable inertial sensor data. Three inertial sensor units were used in this study and were worn by healthy subjects at key points of upper/lower body limbs (chest, right thigh and left ankle). Three main steps describe the activity recognition process: sensors' placement, data pre-proces...

In this time of large-scale planning and land management on public lands, managers are increasingly looking for faster and less expensive methods of data collection. In efforts to make better decisions, planners need to be able to look at changes over time to assess trends. Policy makers are also looking to assess the effects of policies such as prescribed fire or fire suppression. All of these...

We introduce a boosting framework to solve a classification problem with added manifold and ambient regularization costs. It allows for a natural extension of boosting into both semisupervised problems and unsupervised problems. The augmented cost is minimized in a greedy, stagewise functional minimization procedure as in GradientBoost. Our method provides insights into generalization issues in...

This project use migrating means clustering unsupervised classification (MMC), maximum likelihood classification (MLC) trained by picked training samples and trained by the results of unsupervised classification (Hybrid Classification) to classify a 512 pixels by 512 lines NOAA-14 AVHRR Local Area Coverage (LAC) image. All the channels including ch3 and ch3t are used in this project. The image ...

We develop a fully discriminative learning approach for supervised Latent Dirichlet Allocation (LDA) model, which maximizes the posterior probability of the prediction variable given the input document. Different from traditional variational learning or Gibbs sampling approaches, the proposed learning method applies (i) the mirror descent algorithm for exact maximum a posterior inference and (i...

The use of unsupervised data in addition to supervised data has lead to a significant improvement when training discriminative neural networks. However, the best results were achieved with a training process that is divided in two parts: first an unsupervised pre-training step is done for initializing the weights of the network and after these weights are refined with the use of supervised data...