assignment problem (in the new max_cost_assignment() routine).

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of delimited tokens.

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from compiling.

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Previously it only worked up to 5 arguments.

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use general functions as callbacks (via any_function).  This way,
if you have a C++0x compiler, you can use lambda functions with the
event handlers.

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to any_functions.

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is_set() to the any_function so that it's interface is similar to the
member_function_pointer.

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upcoming C++0x standard.  I added this so dlib can be modified to easily support lambda
functions while still being compilable on compilers which don't support the new
std::function.

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distance_functions with no basis vectors in them were added or subtracted.

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improved its specification a bit.

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a global function called approximate_distance_function() which performs the main optimization.  The
reduced_decision_function_trainer2 now depends on this global function.  This changes makes this
function optimizer available for other purposes besides use in the reduced_decision_function_trainer2
object.

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rather than just a simple struct.  I also added overloaded +, -, *, and / operators
for this object so you can do the kind of arithmetic you would expect on an object
which represents a point in a vector space.  This breaks backwards compatibility
with the previous interface though as the member variables are now private.

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clang compiler.

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This has been fixed.

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The bug details are below:
    dlib GUI applications don't work anymore after I upgraded to Ubuntu 10.10 (AMD64). They
    consistently crash in base_window::set_title. XwcTextListToTextProperty returns
    XLocaleNotSupported instead of initializing 'property', but dlib ignores the return value,
    resulting in a crash in XLib when XSetWMName is called with garbage input or when
    property.value is XFreed without having been allocated.

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was defined.

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linear ridge regression and the krr_trainer which uses the empirical_kernel_map
to do non-linear ridge regression.  No changes were made to the behavior of the
krr_trainer.  This update is just to allow the use of linear ridge regression
without a superfluous empirical_kernel_map running at the beginning of the training
process.

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EKM.  Instead, the load() function now will accept non-empty lisf objects
and throw a non-fatal exception.  This behavior should be slightly less
surprising to users, especially since certain degenerate datasets can give
rise to empty lisf objects when you might not expect it.

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