Learning More Efficient Language Models by Discounting the Effect of Words in Regular Expressions – Probabilistic models offer one of the most basic models for learning. However, they are limited in the number of hypotheses and the data structure they rely on. In this paper, we address these issues by modeling the probability of words in sentences as a function of word-level dependencies. We provide a non-parametric model based on the distribution between word pairs and a Bayesian model of distribution parameters of words, which is able to account for word-level dependencies. We also describe how to exploit the knowledge in our model to improve performance of the model. Specifically, we present a novel approach for the construction of an efficient model for word-level dependency based on conditional independence measures for determining the probability of a sentence to be written. Finally, we evaluate our model on both text and sentence-specific benchmark datasets and show how the proposed approach improves the prediction performance.

We present an approach to color object segmentation that incorporates multiscale image segmentation. This method leverages several multiscale image segmentation methods: Histogram, Histogram-Segmentation (HSD), Histogram-Multi-Segmentation (NSE) and Multiscale-Segmentation (NMSE). This approach first attempts to segment each image into its multiscale components, using the multiscale color images. Then it compares the segmentation results with their multiscale counterparts based on the color images in the multiscale components. These multiscales are compared through an adaptive classification procedure. Our approach uses a multi-stage method that assigns a weight to each segment. As a consequence, the segmentation results are more accurate and can be compared with the ones in the multiscale components and the ones in the color images. The proposed method is evaluated on 10 challenging color object segmentation datasets.

The Largest Linear Sequence Regression Model for Sequential Data

Deep Neural Network-Focused Deep Learning for Object Detection

Learning More Efficient Language Models by Discounting the Effect of Words in Regular Expressions

A Review on Fine Tuning for Robust PCA

A deep learning approach to color vision for elderly visual mappingWe present an approach to color object segmentation that incorporates multiscale image segmentation. This method leverages several multiscale image segmentation methods: Histogram, Histogram-Segmentation (HSD), Histogram-Multi-Segmentation (NSE) and Multiscale-Segmentation (NMSE). This approach first attempts to segment each image into its multiscale components, using the multiscale color images. Then it compares the segmentation results with their multiscale counterparts based on the color images in the multiscale components. These multiscales are compared through an adaptive classification procedure. Our approach uses a multi-stage method that assigns a weight to each segment. As a consequence, the segmentation results are more accurate and can be compared with the ones in the multiscale components and the ones in the color images. The proposed method is evaluated on 10 challenging color object segmentation datasets.