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Paweł Wieczyński

Publications and source records attributed to Paweł Wieczyński.

2 recordsLinked to original sources

Identifiability of finite-state $ε$-machines from finite summaries

We study how much observable finite-dimensional information is sufficient to identify a finite-state generator $ε$-machine. The observable summaries are labeled tables of conditional probabilities given finite histories and contiguous block laws. For stationary ergodic finite-alphabet processes, we first prove that a table with past horizon $L$ and future horizon $R$ is equivalent to the block law of length $L+R$. We then recall the classical linear-realization bound. Two hidden Markov presentations with $r$ and $s$ states agree on the entire process law if they agree on words through length $r+s-1$. Our main result gives a structural synchronization bound adapted to exact finite-state generator $ε$-machines. If their target synchronization radii are $a$ and $b$, and their maximum predictive separation radius is $d$, then equality of one block law of length $a+b+d+1$ implies isomorphism. The proof aligns the states of both machines with words that synchronize the two generators simultaneously. For the full-support binary context subfamily of order $m$ with pairwise distinct parameters, the structural synchronization bound is $2m+2$, whereas the state-count and rank-dimension forms of the classical linear-realization bound are $2^{m+1}-1$. A construction shows that a horizon of order $m$ is necessary. When the order-$m$ context structure is known in advance, the sharp horizon is $m+1$ symbols. Thus the raw state-count and rank-dimension formulas can be exponentially pessimistic on this subfamily. The comparison is between general sufficient bounds, not with an optimal realization horizon.

math.PR↗

Mercer Large-Scale Kernel Machines from Ridge Function Perspective

To present Mercer large-scale kernel machines from a ridge function perspective, we recall the results by Lin and Pinkus from {\it Fundamentality of ridge functions}. We consider the main result of the recent paper by Rachimi and Recht, 2008, {\it Random features for large-scale kernel machines} from the Approximation Theory point of view. We study which kernels could be approximated by a sum of products of cosine functions with arguments depending on $x$ and $y$ and present the obstacles of such an approach. The results of this article are applied to Image Processing by procedure "one-vs-rest".

cs.LG↗