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Comparative Study of Instance Based Learning and Back Propagation for Classification Problems

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Multiple Instance Learning

authors discuss the most important algorithms for MIL such as classification, regression and clustering. With a focus on classification, a;

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Data Classification

areas as pattern recognition, database, data mining, and machine learning. Addressing the work of these different communities in a unified way;

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Data Classification

areas as pattern recognition, database, data mining, and machine learning. Addressing the work of these different communities in a unified way;

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Deep Learning for Physical Scientists - Accelerating Research with Machine Learning

basic classification and regression with perceptrons An exploration of training algorithms, including back propagation and stochastic gradient;

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Problems and Answers in Wave Optics

selected problems and answers, this study aid is a powerful supplement to the study of wave optics. Covering the basics of wave propagation;

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Graphical Models for Machine Learning and Digital Communication

A variety of problems in machine learning and digital communication deal with complex but structured natural or artificial systems. In this;

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The Study of Comparative Government and Politics (Routledge Library Editions:Political Science Volume 10)

Originally published in 1957, the first part of the book discusses the general problems of approach, classification, typology and;

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Neural Networks and Qualitative Physics

are used to obtain a 'learning algorithm' of pattern classification problems, such as the back-propagation formula, as well as learning;

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Neural Networks And Qualitative Physics

are used to obtain a 'learning algorithm' of pattern classification problems, such as the back-propagation formula, as well as learning;

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Angular and Deep Learning Pocket Primer

applications. The fourth chapter introduces you to deep learning, the problems it can solve, and some challenges for the future. You will also learn;

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Introduction to Learning and Behavior, International Edition

Based on the latest research available, INTRODUCTION TO LEARNING AND BEHAVIOR, 4E, International Edition equips readers with a clear;

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Fusion Methods for Time-Series Classification

classification is surveyed and five new techniques are presented. Four out of them aim at making the recognition more accurate, while the proposed;

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Support Vector Machines

SVMs for binary classification problems, SVMs for solving multi-classification problems based on ordinal regression, SVMs for semi-supervised;

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Natural Language Processing

fundamental mathematical and deep learning models for NLP under a unified framework. NLP problems are systematically organised by their machine;

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Machine Learning with Scala Quick Start Guide

covers tree-based ensemble techniques for solving both classification and regression problems. Moving ahead, it covers unsupervised learning;

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Neural Network Learning

learning problems, and addresses the key statistical and computational questions. Research on pattern classification with binary-output networks;

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Neural Network Learning

learning problems, and addresses the key statistical and computational questions. Research on pattern classification with binary-output networks;

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A Comparative Study of Funding Shareholder Litigation

approaches being used in financing shareholder litigation in the current world. For instance, Israel and Canada (Quebec and Ontario) are probably;

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Indian Medicinal Plants

taxonomic classifications based on the Bentham and Hooker system of classification. Salient Features: Provides traditional methods;

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Advanced Image and Video Processing Using MATLAB

methods with computer vision applications, covering topics such as event recognition based on deep learning,dynamic scene classification based on;

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Radiowave Propagation

propagation mechanisms is essential for the communications engineer and this text offers the necessary background knowledge. It emphasises the methods;

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Error Propagation in Environmental Modelling With Gis

of error propagation techniques for use in environmental modelling with GIS. Its purpose is to provide a methodology for handling error and error;

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Error Propagation in Environmental Modelling with GIS

of error propagation techniques for use in environmental modelling with GIS. Its purpose is to provide a methodology for handling error and error;

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Linear Algebra and Optimization for Machine Learning

analysis. Numerous machine learning applications have been used as examples, such as spectral clustering, kernel-based classification, and outlier;

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Linear Algebra and Optimization for Machine Learning

analysis. Numerous machine learning applications have been used as examples, such as spectral clustering, kernel-based classification, and outlier;

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Development of Two Hybrid Classification Methods for Machine Learning

In this work two studies are done and they are referred as first study which is named "A Hybrid Classification Method Using Bayesian, K;

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