439,99 zł

Ensemble Methods

Książki Obcojęzyczne>Angielskie>Computing & information technology>Computer programming / software development>Algorithms & data structure...

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Sklep: Gigant.pl

231,03 zł

XGBoost. The Extreme Gradient Boosting for Mining Applications Grin Publishing

Książki / Literatura obcojęzyczna

Technical Report from the year 2017 in the subject Computer Science - Internet, New Technologies, grade: 8, , language: English, abstract: Tree boosting has empirically proven to be a highly effective and versatile approach for data-driven modelling. The core argument is that tree boosting can adaptively determine the local neighbourhoods of the model thereby taking the bias-variance trade-off into consideration during model fitting.Recently, a tree boosting method known as XGBoost has gained popularity by providing higher accuracy. XGBoost further introduces some improvements which allow it to deal with the bias-variance trade-off even more carefully. In this research work, we propose to demonstrate the use of an adaptive procedure i.e. Learned Loss (LL) to update the loss function as the boosting proceeds.Accuracy of the proposed algorithm i.e. XGBoost with Learned Loss boosting function is evaluated using test/train method, K-fold cross validation, and Stratified cross validation method and compared with the state of the art algorithms viz. XGBoost, AdaBoost, AdaBoost-NN, Linear Regression(LR),Neural Network(NN), Decision Tree(DT), Support Vector Machine(SVM), bagging-DT, bagging-NN and Random Forest algorithms. The parameters evaluated are accuracy, Type 1 error and Type 2 error (in Percentages). This study uses total ten years of historical data from Jan 2007 to Aug 2017 of two stock market indices CNX Nifty and S&P BSE Sensex which are highly voluminous.Further, in this research work, we will investigate how XGBoost differs from the more traditional ensemble techniques. Moreover, we will discuss the regularization techniques that these methods offer and the effect these have on the models.In addition to this, we will attempt to answer the question of why XGBoost seems to win so many competitions. To do this, we will provide some arguments for why tree boosting, and in particular XGBoost, seems to be such a highly effective and versatile approach to predictive modelling. The core argument is that tree boosting can be seen to adaptively determine the local neighbourhoods of the model. Tree boosting can thus be seen to take the bias-variance trade off into consideration during model fitting. XGBoost further introduces some improvements which allow it to deal with the bias-variance trade off even more carefully.

Sklep: Libristo.pl

716,73 zł

Fundamentals of Data Science ACADEMIC PR INC

Książki / Literatura obcojęzyczna

Fundamentals of Data Science: Theory and Practicepresents basic and advanced concepts in data science along with real-life applications. The book provides students, researchers, and professionals at different levels a good understanding of the concepts of data science, machine learning, data mining, and analytics. Data science is an evolving area of study that is extensively used in solving real-life problems. It is not just about machine learning, statistics, or databases. Instead, it is a comprehensive study of a number of topics that help extract novel knowledge from data, starting with preparing the data, applying suitable intelligent learning models, and interpreting the outcome. The models applied are not

Sklep: Libristo.pl

11,15 zł

Spalona Żywcem Wyd. Kieszonkowe - Souad

Książki & Multimedia > Książki

Opis - Pierwsze na świecie świadectwo ofiary zbrodni honorowej. Miała siedemnaście lat i zakochała się: zhańbiła rodzinę. Więc rodzina wydała na nią wyrok śmierci... Pokochała go pierwszą miłością. Myślała, że się z nią ożeni. Ale ukochany zniknął, a ona odkryła, że jest w ciąży. A w jej świecie to najcięższa zbrodnia... W zapomnianej przez Boga wiosce w Cisjordanii kobiety są warte mniej niż zwierzęta domowe. Tu mężczyzna jest panem życia i śmierci żony, córki, siostry. Brat może bezkarnie zabić siostrę, matka - córkę, kolejną bezużyteczną dziewczynkę, jaka się urodzi. Tu kobiecie odbiera się godność, a nawet życie zgodnie z odwiecznym obyczajem i uświęconą tradycją. A śmierć jest karą dla dziewczyny, która zhańbi rodzinę. Tak jak Souad. Wyrok wydaje jej ojciec. Szwagier dokonuje egzekucji. Oblewa Souad benzyną i podpala... SOUAD przeżyła - cudem, ale rodzina usiłowała zabić ją nawet w szpitalu. Na zawsze jednak pozostanie straszliwie okaleczona - na ciele i duszy. I wciąż musi się ukrywać; dopóki żyje, jej rodzinę okrywa hańba. Spalona żywcem, opublikowana pod pseudonimem szokująca opowieść o piekle, jakim było jej dzieciństwo i młodość, stała się międzynarodowym bestsellerem. Wydana w 37 w krajach książka przerywa tabu milczenia wobec istniejącej nadal w krajach muzułmańskich barbarzyńskiej tradycji. Nieludzkiego obyczaju, prawa mężczyzn, na mocy którego co najmniej pięć tysięcy kobiet pada co roku ofiarą zbrodni honorowej. Nazwa - Spalona Żywcem Wyd. Kieszonkowe Autor - Souad Oprawa - Miękka Wydawca - Amber Kod ISBN - 9788324159406 Kod EAN - 9788324159406 Wydanie - 1 Rok wydania - 2016 Tłumacz - 31182,maria rostworowska; Format - 110 x 175 x 14 Ilość stron - 224 Podatek VAT - 5% Premiera - 2016-06-23

Sklep: InBook.pl

228,32 zł

Outlier Ensembles Springer International Publishing AG

Książki / Literatura obcojęzyczna

This book discusses a variety of methods for outlier ensembles and organizes them by the specific principles with which accuracy improvements are achieved. In addition, it covers the techniques with which such methods can be made more effective. A formal classification of these methods is provided, and the circumstances in which they work well are examined. The authors cover how outlier ensembles relate (both theoretically and practically) to the ensemble techniques used commonly for other data mining problems like classification. The similarities and (subtle) differences in the ensemble techniques for the classification and outlier detection problems are explored. These subtle differences do impact the design of ensemble algorithms for the latter problem. This book can be used for courses in data mining and related curricula. Many illustrative examples and exercises are provided in order to facilitate classroom teaching. A familiarity is assumed to the outlier detection problem and also to generic problem of ensemble analysis in classification. This is because many of the ensemble methods discussed in this book are adaptations from their counterparts in the classification domain. Some techniques explained in this book, such as wagging, randomized feature weighting, and geometric subsampling, provide new insights that are not available elsewhere. Also included is an analysis of the performance of various types of base detectors and their relative effectiveness. The book is valuable for researchers and practitioners for leveraging ensemble methods into optimal algorithmic design.

Sklep: Libristo.pl

411,72 zł

Outlier Analysis Springer International Publishing AG

Książki / Literatura obcojęzyczna

This book provides comprehensive coverage of the field of outlier analysis from a computer science point of view. It integrates methods from data mining, machine learning, and statistics within the computational framework and is therefore likely to appeal to multiple communities. The chapters of this book can be organized into three categories: Basic algorithms: Chapters 1 through 7 discuss the fundamental algorithms for outlier analysis, including probabilistic and statistical methods, linear methods, proximity-based methods, high-dimensional (subspace) methods, ensemble methods, and supervised methods.Domain-specific methods: Chapters 8 through 12 discuss outlier detection algorithms for various domains of data, such as text, categorical data, time-series data, discrete sequence data, spatial data, and network data.Applications: Chapter 13 is devoted to various applications of outlier analysis. Some guidance is also provided for the practitioner. New in this edition: The second edition of this book is more detailed and appeals to both researchers and practitioners. Significant new material has been added on topics such as kernel methods, one-class support-vector machines, matrix factorization, neural networks, outlier ensembles, time-series methods, and subspace methods. It is written as a textbook and can be used for classroom teaching. A solution manual is available for the numerous exercises at the end of the book.

Sklep: Libristo.pl

411,72 zł

Outlier Analysis Springer International Publishing AG

Książki / Literatura obcojęzyczna

This book provides comprehensive coverage of the field of outlier analysis from a computer science point of view. It integrates methods from data mining, machine learning, and statistics within the computational framework and therefore appeals to multiple communities. The chapters of this book can be organized into three categories: Basic algorithms: Chapters 1 through 7 discuss the fundamental algorithms for outlier analysis, including probabilistic and statistical methods, linear methods, proximity-based methods, high-dimensional (subspace) methods, ensemble methods, and supervised methods.Domain-specific methods: Chapters 8 through 12 discuss outlier detection algorithms for various domains of data, such as text, categorical data, time-series data, discrete sequence data, spatial data, and network data.Applications: Chapter 13 is devoted to various applications of outlier analysis. Some guidance is also provided for the practitioner. The second edition of this book is more detailed and is written to appeal to both researchers and practitioners. Significant new material has been added on topics such as kernel methods, one-class support-vector machines, matrix factorization, neural networks, outlier ensembles, time-series methods, and subspace methods. It is written as a textbook and can be used for classroom teaching.

Sklep: Libristo.pl

436,85 zł

Machine Learning for Text Springer International Publishing AG

Książki / Literatura obcojęzyczna

Text analytics is a field that lies on the interface of information retrieval,machine learning, and natural language processing, and this textbook carefully covers a coherently organized framework drawn from these intersecting topics. The chapters of this textbook is organized into three categories: - Basic algorithms: Chapters 1 through 7 discuss the classical algorithms for machine learning from text such as preprocessing, similarity computation, topic modeling, matrix factorization, clustering, classification, regression, and ensemble analysis. - Domain-sensitive mining: Chapters 8 and 9 discuss the learning methods from text when combined with different domains such as multimedia and the Web. The problem of information retrieval and Web search is also discussed in the context of its relationship with ranking and machine learning methods. - Sequence-centric mining: Chapters 10 through 14 discuss various sequence-centric and natural language applications, such as feature engineering, neural language models, deep learning, text summarization, information extraction, opinion mining, text segmentation, and event detection.This textbook covers machine learning topics for text in detail. Since the coverage is extensive,multiple courses can be offered from the same book, depending on course level. Even though the presentation is text-centric, Chapters 3 to 7 cover machine learning algorithms that are often used indomains beyond text data. Therefore, the book can be used to offer courses not just in text analytics but also from the broader perspective of machine learning (with text as a backdrop).This textbook targets graduate students in computer science, as well as researchers, professors, and industrial practitioners working in these related fields. This textbook is accompanied with a solution manual for classroom teaching.

Sklep: Libristo.pl

374,84 zł

Recommender Systems Springer International Publishing AG

Książki / Literatura obcojęzyczna

This§book comprehensively covers the topic of recommender systems, which provide§personalized recommendations of products or services to users based on their§previous searches or purchases. Recommender system methods have been adapted to§diverse applications including query log mining, social networking, news§recommendations, and computational advertising. This book synthesizes both§fundamental and advanced topics of a research area that has now reached§maturity. The chapters of this book are organized into three categories:§§- Algorithms and evaluation: These§chapters discuss the fundamental algorithms in recommender systems, including§collaborative filtering methods, content-based methods, knowledge-based§methods, ensemble-based methods, and evaluation.§§- Recommendations in specific domains and contexts: the context of a recommendation§can be viewed as important side information that affects the recommendation§goals. Different types of context such as temporal data, spatial data, social§data, tagging data, and trustworthiness are explored.§§- Advanced topics and applications:§Various robustness aspects of recommender systems, such as shilling§systems, attack models, and their defenses are discussed.§§In§addition, recent topics, such as learning to rank, multi-armed bandits, group§systems, multi-criteria systems, and active learning systems, are introduced§together with applications.§§Although this book primarily serves as a§textbook, it will also appeal to industrial practitioners and researchers due§to its focus on applications and references. Numerous examples and exercises§have been provided, and a solution manual is available for instructors.

Sklep: Libristo.pl

Sklepy zlokalizowane w miastach: Warszawa, Kraków, Łódź, Wrocław, Poznań, Gdańsk, Szczecin, Bydgoszcz, Lublin, Katowice

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