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150
0.65
V R 2
^
^
(h=0.5)
R 2
0.6
(h=0.001)
^
* (h=0.5)
R 2
R 2
0.55
^
* (h=0.005)
R 2
100
0.5
0.45
0.4
50
0.35
0.3
w 0
w 0
0
0.25
−1
0
1
2
3
4
−1
0
1
2
3
4
(a)
(b)
V R 2
V R 2
Fig. 3.30
(a) V R 2
(solid),
(dashed), and
(dotted) of the data splitter as
a function of w 0 for a small value of h ;(b) V R 2
V R 2
and
for a higher value of h .
V R 2 = n 1
2
G ij + n 1
n
2
c 1
i∈ω 1
c 1
i∈ω 1
G ij +2 c
i∈ω 1
G ij
n
j∈ω 1
j∈ω 1
j∈ω 1
= q 2 V R 2 |− 1 + p 2 V R 2 | 1 +2 c
i∈ω 1
V R 2 +2 c
i∈ω 1
G ij =
G ij .
j∈ω 1
j∈ω 1
Entropy is therefore decomposed as a weighted sum of positive class-
conditional potentials (as in the theoretic derivation of Appendix C), denoted
V R 2 , plus a term that exclusively relates to cross-errors. Figure 3.30 compares
the behavior of V R 2 , V R 2 ,and V R 2 as a function of the split parameter w 0
for the same problem as in Fig. 3.28. From its inspection we first note the
minimum of V R 2 , corresponding to the entropy maximum at the optimal so-
lution w 0 =0 . 5. Analyzing the behavior of V R 2 ,and V R 2 as in Fig. 3.30a it is
possible to confirm the convergence of both terms towards V R 2
0.
If h is increased above a certain value, V R 2 ,and V R 2 will exhibit a maximum
at w 0 , but with an important difference: while the V R 2 maximum is not a
global one (for any h ), the maximum of V R 2 is a global one.
Thus, to maximize the empirical information V R 2 , it is important not only
to maximize q 2 V R 2 |− 1 + p 2 V R 2 | 1 as for the theoretical counterpart (which
can be achieved with different PDF configurations with consequences to the
classifier performance; see Property 1 of Sect. 2.3.4) but also to maximize
with h
2 c i∈ω 1 j∈ω 1 G ij (with no theoretical counterpart), which is achieved if
the errors are concentrated at the origin. This cross-error term is due to kernel
smoothing.
Note that this analysis also justifies that a larger h is needed to obtain
an information maximum for a larger overlap of the class-conditional error
PDFs.
 
 
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