Matrix: Basics and Usage in MATLAB

In the previous section of the blog, I've discussed the Digital Image Fundamentals that describes the basics, structure, and types of digital images. In this section, we will learn about the fundamentals of Matrix which is necessary as the prerequisite for understanding MATLAB. A matrix is an mxn array of finite numbers each drawn from a field F. m and n are referred to as the number of rows and columns of the matrix respectively, and each of the finite numbers is called the element of…

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FUNDAMENTALS OF DIGITAL IMAGES

Fundamentals of Digital Images These days we encounter hundreds of digital images on a daily basis in our smartphones, laptops, i-pads, etc. But have you ever wondered how these images are generated, stored and transferred through various networks? Let’s explore this in the fundamentals of digital images by first addressing the most basic question: what actually constitutes a digital image? A digital image, in its most basic form, is a collection of numbers arranged in two or three dimensions (called matrix) such that…

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NVIDIA Jetson Nano Developer Kit

In this tutorial you will learn how to prepare your NVIDIA Jetson Nano Developer Kit for first boot. Introduction to NVIDIA Jetson Nano Developer Kit The Jetson Nano Developer Kit is a small AI computer having quad-core 64bit ARM Cortex-A57 CPU with 128 GPU (CUDA) cores capable of doing 472 GFLOPS of computation- suitable for learners, makers, and developers. One can easily build practical AI applications, AI robots, run Docker containers and many more with this small yet powerful super computing Jetson device.…

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Fast and Robust Wrapper Method for N-gram Feature Template Induction in Structured Prediction

Fast and Robust Wrapper Method for N-gram Feature Template Induction in Structured Prediction Fast and Robust Wrapper Method for N-gram Feature Template Induction in Structured Prediction ABSTRACT N-gram feature templates that consider consecutive contextual information comprise a family of important feature templates used in structured prediction. Some previous studies considered the n-gram feature selection problem but they focused on one or several types of features in certain tasks, e.g., consecutive words in a text categorization task. In this paper, we propose a fast…

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Feature selection based on a normalized difference measure for text classification

Feature selection based on a normalized difference measure for text classification Feature selection based on a normalized difference measure for text classification   a b s t r a c t The goal of feature selection in text classification is to choose highly distinguishing fea- tures for improving the performance of a classifier. The well-known text classification fea- ture selection metric named balanced accuracy measure (ACC2) (Forman, 2003) evaluates a term by taking the difference of its document frequency in the positive class…

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Classification of text documents based on score level fusion approach

Classification of text documents based on score level fusion approach Classification of text documents based on score level fusion approach   a b s t r a c t Text document classification is a well known theme in the field of the information retrieval and text min- ing. Selection of most desired features in the text document plays a vital role in classification problem. This research article addresses the problem of text classification by considering Sentence–Vector Space Model (S-VSM) and Unigram representation models…

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A feature selection model based on genetic rank aggregation for text sentiment classification

A feature selection model based on genetic rank aggregation for text sentiment classification A feature selection model based on genetic rank aggregation for text sentiment classification   Abstract Sentiment analysis is an important research direction of natural language processing, text mining and web mining which aims to extract subjective information in source materials. The main challenge encountered in machine learning method-based sentiment classification is the abundant amount of data available. This amount makes it difficult to train the learning algorithms in a feasible…

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Automatic Cross-Language Retrieval Using Latent Semantic Indexing

Automatic Cross-Language Retrieval Using Latent Semantic Indexing Automatic Cross-Language Retrieval Using Latent Semantic Indexing   Abstract We describe a method for fully automated cross-language documenret trieval in whichn o queryt ranslation is required. Queriesi n one languagec an retrieve documenitns                    other languages (as well as the original language). This is accomplished by a methodth at automaticallyc onstructs a multilingual semantic space using Latent Semantic                       …

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TERM-WEIGHTING APPROACHES IN AUTOMATIC TEXT RETRIEVAL

TERM-WEIGHTING APPROACHES IN AUTOMATIC TEXT RETRIEVAL TERM-WEIGHTING APPROACHES IN AUTOMATIC TEXT RETRIEVAL   Abstract- The experimental evidence accumulated over the past 20 years indicates that text indexing systems based on the assignment of appropriately weighted single terms produce retrieval results that are superior to those obtainable with other more elaborate text representations. These results depend crucially on the choice of effective term weighting systems. This article summarizes the insights gained in automatic term weighting, and provides baseline single-term-indexing models with which other more…

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Machine Learning in Automated Text Categorization

Machine Learning in Automated Text Categorization Machine Learning in Automated Text Categorization Abstract: The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last 10 years, due to the increased availability of documents in digital form and the ensuing need to organize them. In the research community the dominant approach to this problem is based on machine learning techniques: a general inductive process automatically builds a classifier by learning, from a set of preclassified documents, the…

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