Ensemble

"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.

This definitive history brings Chicago’s celebrated theater and comedy scenes to life with stories from some of its biggest stars spanning sixty-five years. Chicago is a bona fide theater town, bursting with vitality that thrills local fans and produces generation after generation of world-renowned actors, directors, playwrights, and designers. Now Mark Larson shares the rich theatrical history of Chicago through first-person accounts from the people who made it. Drawing from more than three hundred interviews, Larson weaves a narrative that expresses the spirit of Chicago’s ensemble ethos: the voices of celebrities such as Julia Louis-Dreyfus, Ed Asner, George Wendt, Michael Shannon, and Tracy Letts comingle with stories from designers, composers, and others who have played a crucial role in making Chicago theater so powerful, influential, and unique. Among many other topics, this book explores the early days of the fabled Compass Players and the legendary Second City in the ‘50s and ‘60s; the rise of acclaimed ensembles like Steppenwolf in the ‘70s; the explosion of storefront and neighborhood companies in the ‘80s; and the enduring global influence of the city as the center of improv training and performance.

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.

This updated compendium provides a methodical introduction with a coherent and unified repository of ensemble methods, theories, trends, challenges, and applications. More than a third of this edition comprised of new materials, highlighting descriptions of the classic methods, and extensions and novel approaches that have recently been introduced.Along with algorithmic descriptions of each method, the settings in which each method is applicable and the consequences and tradeoffs incurred by using the method is succinctly featured. R code for implementation of the algorithm is also emphasized.The unique volume provides researchers, students and practitioners in industry with a comprehensive, concise and convenient resource on ensemble learning methods.

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 cover

(Meredith Music Percussion). This two-volume publication provides guidelines on percussion player and instrument requirements for over 2,000 concert band and wind ensemble works. It contains helpful information for conductors, section leaders, stage managers, equipment managers and ensemble librarians. An incredible compilation for school, college, military, community and professional bands and wind ensembles. (a href="http://youtu.be/OVqEyKf5JnU" target="_blank")Click here for a YouTube video on Percussion Assignments for Band and Wind Ensemble(/a)

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.