Classification Algorithms for Codes and Designs
Classification Algorithms for Codes and Designs

Author: Petteri Kaski

Publisher: Springer Science & Business Media

Published: 2006-02-03

Total Pages: 415

ISBN-13: 3540289917

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A new starting-point and a new method are requisite, to insure a complete [classi?cation of the Steiner triple systems of order 15]. This method was furnished, and its tedious and di?cult execution und- taken, by Mr. Cole. F. N. Cole, L. D. Cummings, and H. S. White (1917) [129] The history of classifying combinatorial objects is as old as the history of the objects themselves. In the mid-19th century, Kirkman, Steiner, and others became the fathers of modern combinatorics, and their work – on various objects, including (what became later known as) Steiner triple systems – led to several classi?cation results. Almost a century earlier, in 1782, Euler [180] published some results on classifying small Latin squares, but for the ?rst few steps in this direction one should actually go at least as far back as ancient Greece and the proof that there are exactly ?ve Platonic solids. One of the most remarkable achievements in the early, pre-computer era is the classi?cation of the Steiner triple systems of order 15, quoted above. An onerous task that, today, no sensible person would attempt by hand calcu- tion. Because, with the exception of occasional parameters for which com- natorial arguments are e?ective (often to prove nonexistence or uniqueness), classi?cation in general is about algorithms and computation.

Machine Learning Models and Algorithms for Big Data Classification
Machine Learning Models and Algorithms for Big Data Classification

Author: Shan Suthaharan

Publisher: Springer

Published: 2015-10-20

Total Pages: 364

ISBN-13: 1489976418

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This book presents machine learning models and algorithms to address big data classification problems. Existing machine learning techniques like the decision tree (a hierarchical approach), random forest (an ensemble hierarchical approach), and deep learning (a layered approach) are highly suitable for the system that can handle such problems. This book helps readers, especially students and newcomers to the field of big data and machine learning, to gain a quick understanding of the techniques and technologies; therefore, the theory, examples, and programs (Matlab and R) presented in this book have been simplified, hardcoded, repeated, or spaced for improvements. They provide vehicles to test and understand the complicated concepts of various topics in the field. It is expected that the readers adopt these programs to experiment with the examples, and then modify or write their own programs toward advancing their knowledge for solving more complex and challenging problems. The presentation format of this book focuses on simplicity, readability, and dependability so that both undergraduate and graduate students as well as new researchers, developers, and practitioners in this field can easily trust and grasp the concepts, and learn them effectively. It has been written to reduce the mathematical complexity and help the vast majority of readers to understand the topics and get interested in the field. This book consists of four parts, with the total of 14 chapters. The first part mainly focuses on the topics that are needed to help analyze and understand data and big data. The second part covers the topics that can explain the systems required for processing big data. The third part presents the topics required to understand and select machine learning techniques to classify big data. Finally, the fourth part concentrates on the topics that explain the scaling-up machine learning, an important solution for modern big data problems.

Classification Algorithms
Classification Algorithms

Author: Mike James

Publisher: Wiley-Interscience

Published: 1932

Total Pages: 209

ISBN-13:

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An introduction to the theory and application of classification analysis. Presents techniques for classification analysis that can be applied to a range of disciplines, including biology, medicine, artificial intelligence, and others. Gives implementations of classification algorithms in BASIC. A chapter is devoted to the impact of pattern recognition and artificial intelligence on classification analysis. Advanced mathematics is kept to a minimum.

Error-Correcting Linear Codes
Error-Correcting Linear Codes

Author: Anton Betten

Publisher: Springer Science & Business Media

Published: 2006-09-21

Total Pages: 819

ISBN-13: 3540317031

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This text offers an introduction to error-correcting linear codes for researchers and graduate students in mathematics, computer science and engineering. The book differs from other standard texts in its emphasis on the classification of codes by means of isometry classes. The relevant algebraic are developed rigorously. Cyclic codes are discussed in great detail. In the last four chapters these isometry classes are enumerated, and representatives are constructed algorithmically.

Learning Classification Algorithms in Data Mining
Learning Classification Algorithms in Data Mining

Author: Swetha Rajendiran

Publisher:

Published: 2015

Total Pages: 154

ISBN-13:

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Classification algorithms are used in data mining to classify data based on class labels. It involves building a model using training data set, and then using the built model to assign given items to specific classes/categories. In the model building process, also called training process, a classification algorithm finds relationships between the attributes of the data and the target. Different classification algorithms use different techniques for finding relationships. These relationships are summarized in a model, which can then be applied to a new data set in which the class assignments are unknown. This project's objective is to create a courseware that focuses on creating materials to achieve the goal of helping the students get deeper understanding of the most used classification algorithms in data mining. The existing materials on the classification algorithms are completely textual and students find it difficult to grasp. By using interactive examples and animated tutorials provided in the courseware, students should be able to intuitively learn these classification algorithms easily. With the help of this courseware, students will be able to learn the algorithms using flash animations and then visualize the steps with the help of interactive examples that can be modified in many ways by the student to get a complete understanding of the algorithms. There is also information provided on how to make practical use of these algorithms using data mining tools such as Weka and RapidMiner where students can apply the algorithms on real datasets available. Implementation of the courseware is done with technologies such as HTML, JavaScript, and Bootstrap CSS.

Demand-Driven Associative Classification
Demand-Driven Associative Classification

Author: Adriano Veloso

Publisher: Springer Science & Business Media

Published: 2011-05-18

Total Pages: 114

ISBN-13: 085729525X

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The ultimate goal of machines is to help humans to solve problems. Such problems range between two extremes: structured problems for which the solution is totally defined (and thus are easily programmed by humans), and random problems for which the solution is completely undefined (and thus cannot be programmed). Problems in the vast middle ground have solutions that cannot be well defined and are, thus, inherently hard to program. Machine Learning is the way to handle this vast middle ground, so that many tedious and difficult hand-coding tasks would be replaced by automatic learning methods. There are several machine learning tasks, and this work is focused on a major one, which is known as classification. Some classification problems are hard to solve, but we show that they can be decomposed into much simpler sub-problems. We also show that independently solving these sub-problems by taking into account their particular demands, often leads to improved classification performance.

Data Classification
Data Classification

Author: Charu C. Aggarwal

Publisher: CRC Press

Published: 2014-07-25

Total Pages: 704

ISBN-13: 1466586753

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Comprehensive Coverage of the Entire Area of Classification Research on the problem of classification tends to be fragmented across such areas as pattern recognition, database, data mining, and machine learning. Addressing the work of these different communities in a unified way, Data Classification: Algorithms and Applications explores the underlying algorithms of classification as well as applications of classification in a variety of problem domains, including text, multimedia, social network, and biological data. This comprehensive book focuses on three primary aspects of data classification: Methods: The book first describes common techniques used for classification, including probabilistic methods, decision trees, rule-based methods, instance-based methods, support vector machine methods, and neural networks. Domains: The book then examines specific methods used for data domains such as multimedia, text, time-series, network, discrete sequence, and uncertain data. It also covers large data sets and data streams due to the recent importance of the big data paradigm. Variations: The book concludes with insight on variations of the classification process. It discusses ensembles, rare-class learning, distance function learning, active learning, visual learning, transfer learning, and semi-supervised learning as well as evaluation aspects of classifiers.

Mastering Classification Algorithms for Machine Learning
Mastering Classification Algorithms for Machine Learning

Author: Partha Majumdar

Publisher: BPB Publications

Published: 2023-05-23

Total Pages: 383

ISBN-13: 935551851X

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A practical guide to mastering Classification algorithms for Machine learning KEY FEATURES ● Get familiar with all the state-of-the-art classification algorithms for machine learning. ● Understand the mathematical foundations behind building machine learning models. ● Learn how to apply machine learning models to solve real-world industry problems. DESCRIPTION Classification algorithms are essential in machine learning as they allow us to make predictions about the class or category of an input by considering its features. These algorithms have a significant impact on multiple applications like spam filtering, sentiment analysis, image recognition, and fraud detection. If you want to expand your knowledge about classification algorithms, this book is the ideal resource for you. The book starts with an introduction to problem-solving in machine learning and subsequently focuses on classification problems. It then explores the Naïve Bayes algorithm, a probabilistic method widely used in industrial applications. The application of Bayes Theorem and underlying assumptions in developing the Naïve Bayes algorithm for classification is also covered. Moving forward, the book centers its attention on the Logistic Regression algorithm, exploring the sigmoid function and its significance in binary classification. The book also covers Decision Trees and discusses the Gini Factor, Entropy, and their use in splitting trees and generating decision leaves. The Random Forest algorithm is also thoroughly explained as a cutting-edge method for classification (and regression). The book concludes by exploring practical applications such as Spam Detection, Customer Segmentation, Disease Classification, Malware Detection in JPEG and ELF Files, Emotion Analysis from Speech, and Image Classification. By the end of the book, you will become proficient in utilizing classification algorithms for solving complex machine learning problems. WHAT YOU WILL LEARN ● Learn how to apply Naïve Bayes algorithm to solve real-world classification problems. ● Explore the concept of K-Nearest Neighbor algorithm for classification tasks. ● Dive into the Logistic Regression algorithm for classification. ● Explore techniques like Bagging and Random Forest to overcome the weaknesses of Decision Trees. ● Learn how to combine multiple models to improve classification accuracy and robustness. WHO THIS BOOK IS FOR This book is for Machine Learning Engineers, Data Scientists, Data Science Enthusiasts, Researchers, Computer Programmers, and Students who are interested in exploring a wide range of algorithms utilized for classification tasks in machine learning. TABLE OF CONTENTS 1. Introduction to Machine Learning 2. Naïve Bayes Algorithm 3. K-Nearest Neighbor Algorithm 4. Logistic Regression 5. Decision Tree Algorithm 6. Ensemble Models 7. Random Forest Algorithm 8. Boosting Algorithm Annexure 1: Jupyter Notebook Annexure 2: Python Annexure 3: Singular Value Decomposition Annexure 4: Preprocessing Textual Data Annexure 5: Stemming and Lamentation Annexure 6: Vectorizers Annexure 7: Encoders Annexure 8: Entropy

Pattern Classification Using Ensemble Methods
Pattern Classification Using Ensemble Methods

Author: Lior Rokach

Publisher: World Scientific

Published: 2010

Total Pages: 242

ISBN-13: 9814271071

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1. Introduction to pattern classification. 1.1. Pattern classification. 1.2. Induction algorithms. 1.3. Rule induction. 1.4. Decision trees. 1.5. Bayesian methods. 1.6. Other induction methods -- 2. Introduction to ensemble learning. 2.1. Back to the roots. 2.2. The wisdom of crowds. 2.3. The bagging algorithm. 2.4. The boosting algorithm. 2.5. The AdaBoost algorithm. 2.6. No free lunch theorem and ensemble learning. 2.7. Bias-variance decomposition and ensemble learning. 2.8. Occam's razor and ensemble learning. 2.9. Classifier dependency. 2.10. Ensemble methods for advanced classification tasks -- 3. Ensemble classification. 3.1. Fusions methods. 3.2. Selecting classification. 3.3. Mixture of experts and meta learning -- 4. Ensemble diversity. 4.1. Overview. 4.2. Manipulating the inducer. 4.3. Manipulating the training samples. 4.4. Manipulating the target attribute representation. 4.5. Partitioning the search space. 4.6. Multi-inducers. 4.7. Measuring the diversity -- 5. Ensemble selection. 5.1. Ensemble selection. 5.2. Pre selection of the ensemble size. 5.3. Selection of the ensemble size while training. 5.4. Pruning - post selection of the ensemble size -- 6. Error correcting output codes. 6.1. Code-matrix decomposition of multiclass problems. 6.2. Type I - training an ensemble given a code-matrix. 6.3. Type II - adapting code-matrices to the multiclass problems -- 7. Evaluating ensembles of classifiers. 7.1. Generalization error. 7.2. Computational complexity. 7.3. Interpretability of the resulting ensemble. 7.4. Scalability to large datasets. 7.5. Robustness. 7.6. Stability. 7.7. Flexibility. 7.8. Usability. 7.9. Software availability. 7.10. Which ensemble method should be used?

Understanding Machine Learning
Understanding Machine Learning

Author: Shai Shalev-Shwartz

Publisher: Cambridge University Press

Published: 2014-05-19

Total Pages: 415

ISBN-13: 1107057132

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Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.