Jayadeva – författare

Jayadeva's Gitagovínda is a lyrical account of the illicit springtime love affair of Krishna and Radha, a god and goddess manifesting on earth as a cowherd and milkmaid for the sake of relishing the sweet miseries and rapturous delights of erotic love. The narrative framing their bucolic songs was composed under royal patronage in northeastern India in the twelfth century. It was to be performed for connoisseurs of poetry and the erotic arts, for aesthetes and voluptuaries who, while sensually engaged, were at the same time devoted to Krishna as Lord of the Universe. The text at once celebrates the vicissitudes of carnal love and the transports of religious devotion, merging and reconciling those realms of emotion and experience. Erotic and religious sensibilities serve, and are served by, the pleasures of poetry. In the centuries following its composition, the courtly text became a vastly popular inspirational hymnal. Jayadeva's songs continue to be sung throughout India in fervent devotional adoration of Krishna.

This book provides a systematic and focused study of the various aspects of twin support vector machines (TWSVM) and related developments for classification and regression. In addition to presenting most of the basic models of TWSVM and twin support vector regression (TWSVR) available in the literature, it also discusses the important and challenging applications of this new machine learning methodology. A chapter on “Additional Topics” has been included to discuss kernel optimization and support tensor machine topics, which are comparatively new but have great potential in applications. It is primarily written for graduate students and researchers in the area of machine learning and related topics in computer science, mathematics, electrical engineering, management science and finance.

This book provides a systematic and focused study of the various aspects of twin support vector machines (TWSVM) and related developments for classification and regression. In addition to presenting most of the basic models of TWSVM and twin support vector regression (TWSVR) available in the literature, it also discusses the important and challenging applications of this new machine learning methodology. A chapter on “Additional Topics” has been included to discuss kernel optimization and support tensor machine topics, which are comparatively new but have great potential in applications. It is primarily written for graduate students and researchers in the area of machine learning and related topics in computer science, mathematics, electrical engineering, management science and finance.