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"Pitch-perfect." —People "You won’t be able to quit these characters." —goop The addictive novel about four young friends navigating the cutthroat world of classical music and their complex relationships with each other, as ambition, passion, and love intertwine over the course of their lives. Jana. Brit. Daniel. Henry. They would never have been friends if they hadn't needed each other. They would never have found each other except for the art which drew them together. They would never have become family without their love for the music, for each other. Brit is the second violinist, a beautiful and quiet orphan; on the viola is Henry, a prodigy who's always had it easy; the cellist is Daniel, the oldest and an angry skeptic who sleeps around; and on first violin is Jana, their flinty, resilient leader. Together, they are the Van Ness Quartet. After the group's youthful, rocky start, they experience devastating failure and wild success, heartbreak and marriage, triumph and loss, betrayal and enduring loyalty. They are always tied to each other - by career, by the intensity of their art, by the secrets they carry, by choosing each other over and over again. Following these four unforgettable characters, Aja Gabel's debut novel gives a riveting look into the high-stakes, cutthroat world of musicians, and of lives made in concert. The story of Brit and Henry and Daniel and Jana, The Ensemble is a heart-skipping portrait of ambition, friendship, and the tenderness of youth.
A detailed road map for wealth managers who want to build an ensemble firm or team and achieve sustained growth, profitability and high valuations Why do ten percent of wealth management firms grow faster than the rest of the industry, often despite the turbulence of the markets? The answer, according to industry consultant and researcher, P. Palaveev, is that the most successful firms are those which, create and promote a team-based service model that serves as the foundation of their enterprise. Find out how and why a team-based service model can play a decisive role in the future growth and sustained success of your wealth management firm Discover the key factors for building a successful ensemble firm and profit from the best practices top team-based firms employ Profit from the author's years of experience working with the world's top wealth management firms and the data he has compiled as a pre-eminent industry researcher Learn about the various organizational structures, partnership models and career path options and how to put them to work building an ensemble practice Get the lowdown on how the savviest traditional broker-dealer firms have formed dynamic ensemble teams within their organizations and learn of the results they've achieved
Accent on Ensembles is an exciting book of duets, trios and quartets for flexible instrumentation that correlates with Accent on Achievement, Book 1. Use these ensembles to develop confidence in young players and as a valuable resource for music during contest season. Since the instrumentation is flexible, any combination of instruments can play together. Accent on Ensembles, Book 2 is an exciting book of duets, trios and quartets for flexible instrumentation that correlates with Accent on Achievement, Book 2.
Ensemble machine learning combines the power of multiple machine learning approaches, working together to deliver models that are highly performant and highly accurate. Inside Ensemble Methods for Machine Learning you will find: Methods for classification, regression, and recommendations Sophisticated off-the-shelf ensemble implementations Random forests, boosting, and gradient boosting Feature engineering and ensemble diversity Interpretability and explainability for ensemble methods Ensemble machine learning trains a diverse group of machine learning models to work together, aggregating their output to deliver richer results than a single model. Now in Ensemble Methods for Machine Learning you’ll discover core ensemble methods that have proven records in both data science competitions and real-world applications. Hands-on case studies show you how each algorithm works in production. By the time you're done, you'll know the benefits, limitations, and practical methods of applying ensemble machine learning to real-world data, and be ready to build more explainable ML systems. About the Technology Automatically compare, contrast, and blend the output from multiple models to squeeze the best results from your data. Ensemble machine learning applies a “wisdom of crowds” method that dodges the inaccuracies and limitations of a single model. By basing responses on multiple perspectives, this innovative approach can deliver robust predictions even without massive datasets. About the Book Ensemble Methods for Machine Learning teaches you practical techniques for applying multiple ML approaches simultaneously. Each chapter contains a unique case study that demonstrates a fully functional ensemble method, with examples including medical diagnosis, sentiment analysis, handwriting classification, and more. There’s no complex math or theory—you’ll learn in a visuals-first manner, with ample code for easy experimentation! What’s Inside Bagging, boosting, and gradient boosting Methods for classification, regression, and retrieval Interpretability and explainability for ensemble methods Feature engineering and ensemble diversity About the Reader For Python programmers with machine learning experience. About the Author Gautam Kunapuli has over 15 years of experience in academia and the machine learning industry. Table of Contents PART 1 - THE BASICS OF ENSEMBLES 1 Ensemble methods: Hype or hallelujah? PART 2 - ESSENTIAL ENSEMBLE METHODS 2 Homogeneous parallel ensembles: Bagging and random forests 3 Heterogeneous parallel ensembles: Combining strong learners 4 Sequential ensembles: Adaptive boosting 5 Sequential ensembles: Gradient boosting 6 Sequential ensembles: Newton boosting PART 3 - ENSEMBLES IN THE WILD: ADAPTING ENSEMBLE METHODS TO YOUR DATA 7 Learning with continuous and count labels 8 Learning with categorical features 9 Explaining your ensembles
An up-to-date, self-contained introduction to a state-of-the-art machine learning approach, Ensemble Methods: Foundations and Algorithms shows how these accurate methods are used in real-world tasks. It gives you the necessary groundwork to carry out further research in this evolving field. After presenting background and terminology, the book covers the main algorithms and theories, including Boosting, Bagging, Random Forest, averaging and voting schemes, the Stacking method, mixture of experts, and diversity measures. It also discusses multiclass extension, noise tolerance, error-ambiguity and bias-variance decompositions, and recent progress in information theoretic diversity. Moving on to more advanced topics, the author explains how to achieve better performance through ensemble pruning and how to generate better clustering results by combining multiple clusterings. In addition, he describes developments of ensemble methods in semi-supervised learning, active learning, cost-sensitive learning, class-imbalance learning, and comprehensibility enhancement.
An exciting new model for streamlining organizational management and structure, from an organization known around the world for its innovative structure. For nearly three decades the Orpheus Chamber Orchestra has earned rave reviews as one of the premier touring musical ensembles in the world. Recently, it has also attracted the attention and admiration of the business world. As the only major orchestra without a conductor, Orpheus is a shining example of a new management style based on collaborative leadership. Leadership Ensemble is designed to show business leaders in any industry how to re-create the successful "Orpheus Process" in their own companies by dismantling top-heavy hierarchies; developing flexible, responsive strategies and decision-making procedures; and unleashing employee creativity, responsibility, and productivity. The authors delineate the ten core principles of collaborative management, including shared and rotated leadership, passionate dedication to mission, clarity of roles within an organization, and learning to listen. Spiced with anecdotes and lessons from the life of Orpheus and case studies from the world of business, Leadership Ensemble is an entertaining, eminently practical guide to implementing changes essential to survival in today's speed-of-light business environment.
Through the past twenty years or so, the "pit," or "front ensemble," has grown to be an integral part of the modern drum corps and marching band activities. This evolution has brought us into a new realm in the world of percussion education and performance, merging outdoor playing styles with indoor playing styles. "Up Front" explores the numerous details of a successful front ensemble. Topics included are: equipment, instrument ranges, transporting, instrument care, technique, exercises, teaching techniques, arranging, and much, much more. At a whopping 225 pages thick, this resource is sure to provide years of insight. It is meant to be an all-encompassing guide for pit members and educators of all experience levels. Jim Casella and Jim Ancona have instructed and arranged for some of drum corps' most musical and innovative percussion ensembles. Now they have combined their "pit knowledge" into one complete resource. "Up Front" explores every unique aspect of today's front ensemble, from technique and musicianship to arranging and instructing.
This book contains the extended papers presented at the 3rd Workshop on Supervised and Unsupervised Ensemble Methods and their Applications (SUEMA) that was held in conjunction with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD 2010, Barcelona, Catalonia, Spain). As its two predecessors, its main theme was ensembles of supervised and unsupervised algorithms – advanced machine learning and data mining technique. Unlike a single classification or clustering algorithm, an ensemble is a group of algorithms, each of which first independently solves the task at hand by assigning a class or cluster label (voting) to instances in a dataset and after that all votes are combined together to produce the final class or cluster membership. As a result, ensembles often outperform best single algorithms in many real-world problems. This book consists of 14 chapters, each of which can be read independently of the others. In addition to two previous SUEMA editions, also published by Springer, many chapters in the current book include pseudo code and/or programming code of the algorithms described in them. This was done in order to facilitate ensemble adoption in practice and to help to both researchers and engineers developing ensemble applications.
This book offers a comprehensive overview of ensemble learning in the field of feature selection (FS), which consists of combining the output of multiple methods to obtain better results than any single method. It reviews various techniques for combining partial results, measuring diversity and evaluating ensemble performance. With the advent of Big Data, feature selection (FS) has become more necessary than ever to achieve dimensionality reduction. With so many methods available, it is difficult to choose the most appropriate one for a given setting, thus making the ensemble paradigm an interesting alternative. The authors first focus on the foundations of ensemble learning and classical approaches, before diving into the specific aspects of ensembles for FS, such as combining partial results, measuring diversity and evaluating ensemble performance. Lastly, the book shows examples of successful applications of ensembles for FS and introduces the new challenges that researchers now face. As such, the book offers a valuable guide for all practitioners, researchers and graduate students in the areas of machine learning and data mining.