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樓主: Encomium
21#
發(fā)表于 2025-3-25 05:42:26 | 只看該作者
Menschenorientiertes Krankenhausmanagementies; ., where the translation of a given string is returned, and ., that are answered either positively or with a counterexample. A probabilistic setting in which equivalence queries are substituted by a random sample oracle is also studied and the corresponding modifications to the algorithm presen
22#
發(fā)表于 2025-3-25 09:31:03 | 只看該作者
https://doi.org/10.1007/978-3-476-05324-4g-distance word pair. To select the most significant trigger pairs, we need suitable criteria which are the topics of this paper. We extend a baseline language model by a single word trigger pair and use the perplexity of this extended language model as selection criterion. This extension is applied
23#
發(fā)表于 2025-3-25 15:13:12 | 只看該作者
https://doi.org/10.1007/978-3-658-18702-6n. Grammars, describing the structure of patterns, are used as kernel functions in a hierarchical agglomerative clustering algorithm, taking a ratio of decrease in grammar complexity as criterion for cluster association. Cluster analysis of a set of contour images is performed to illustrate the prop
24#
發(fā)表于 2025-3-25 16:06:26 | 只看該作者
25#
發(fā)表于 2025-3-25 21:26:22 | 只看該作者
26#
發(fā)表于 2025-3-26 01:24:33 | 只看該作者
27#
發(fā)表于 2025-3-26 06:37:05 | 只看該作者
Jürgen Gotthard,Ulrike Hanemanntended through the use of barriers, reached 87.3 % precision (1.12 tags/word) at 98.2 % recall. The results compare favourably with other methods that are used for similar tasks although they are by no means as good as the results achieved using the original hand-written rules developed over several
28#
發(fā)表于 2025-3-26 11:37:10 | 只看該作者
29#
發(fā)表于 2025-3-26 16:30:01 | 只看該作者
30#
發(fā)表于 2025-3-26 20:26:51 | 只看該作者
Menschenrechte und europ?ische Au?enpolitikdiscrete alphabet . and a source producing an input sequence (..,..,...,..) with .. ∈ ., the task of sequence prediction is to guess the successive sequence element ... Here each element .. may represent an object, a discrete event or any other discrete entity. Prediction with CMM is analogy-based.
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