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Nothing fully prepares one for service in a combat zone. When I was selected to command the NATO Air Training Command Afghanistan and the 438th Air Expeditionary Wing in Kabul, I had almost a quarter century of uniformed service to prepare me for this dynamic assignment. "Dynamic" because the mission would be to develop an independent, self-sustaining Afghan Air Force ... in an active war zone. The book you hold in your hand is a collection of the most important lessons we learned. Lessons which anyone, be it a parent, pastor, politician, soldier, teacher, or office teammate, can use to...

• A classic work in literary and cultural theory.
  
• Reissued to coincide with calls for a return to structuralism.
  
• Includes a new introduction by Jean-Michel Rabate, which explores developments in the reception of structuralist theory in the past five to ten years.

• A classic work in literary and cultural theory.
  
• Reissued to coincide with calls for a return to structuralism.
  
• Includes a new introduction by Jean-Michel Rabate, which explores developments in the reception of structuralist theory in the past five to ten years.

Methods of dimensionality reduction provide a way to understand and visualize the structure of complex data sets. Traditional methods like principal component analysis and classical metric multidimensional scaling suffer from being based on linear models. Until recently, very few methods were able to reduce the data dimensionality in a nonlinear way. However, since the late nineties, many new methods have been developed and nonlinear dimensionality reduction, also called manifold learning, has become a hot topic. New advances that account for this rapid growth are, e.g. the use of graphs...

Methods of dimensionality reduction provide a way to understand and visualize the structure of complex data sets. Traditional methods like principal component analysis and classical metric multidimensional scaling suffer from being based on linear models. Until recently, very few methods were able to reduce the data dimensionality in a nonlinear way. However, since the late nineties, many new methods have been developed and nonlinear dimensionality reduction, also called manifold learning, has become a hot topic. New advances that account for this rapid growth are, e.g. the use of graphs...