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Titlebook: Machine Learning: ECML 2000; 11th European Confer Ramon López de Mántaras,Enric Plaza Conference proceedings 2000 Springer-Verlag Berlin He

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樓主: 你太謙虛
31#
發(fā)表于 2025-3-26 21:43:16 | 只看該作者
Tom Brijs,Gilbert Swinnen,Koen Vanhoof,Geert Wets. These have accelerated the erosion and the silting of the lake basin, as well. In order to correctly assess the source areas of the sediments, from the drainage basin, satellite imagery has been used, to accurately identify the areas that have suffered from deforestation in the last decades. Throu
32#
發(fā)表于 2025-3-27 02:28:57 | 只看該作者
33#
發(fā)表于 2025-3-27 06:24:30 | 只看該作者
Pádraig Cunningham,John Carney, and especially phosphate. Long-term series of field data partially confirm these results. After having been colonized by the mussel, a 47?km. reservoir developed higher concentrations of ammonia and phosphates, a higher P:N ratio, more transparency, less seston, and less phytoplankton and primary
34#
發(fā)表于 2025-3-27 13:26:10 | 只看該作者
35#
發(fā)表于 2025-3-27 14:34:27 | 只看該作者
36#
發(fā)表于 2025-3-27 20:18:10 | 只看該作者
Beyond Occam’s Razor: Process-Oriented Evaluationbly predict good generalization, researchers and practitioners often invoke “Occam’s razor” to select among hypotheses: prefer the simplest hypothesis consistent with the data. Occam’s razor has a long history in science, but a mass of recent evidence suggests that in most cases it is outperformed b
37#
發(fā)表于 2025-3-27 22:33:21 | 只看該作者
The Representation Race — Preprocessing for Handling Time Phenomenaions. This paper emphasizes the importance of constructing an appropriate representation .. for knowledge discovery applications using the example of time related phenomena. Given the same raw data — most frequently a database with time-stamped data — rather different representations have to be prod
38#
發(fā)表于 2025-3-28 02:09:10 | 只看該作者
Short-Term Profiling for a Case-Based Reasoning Recommendation Systemendation using a modified cycle for case-based reasoning in which a new refinement step is introduced. We then use this cycle combined with a heuristic we devised to create a short-term profile of the client. This profile is not stored or reused after the transaction, reducing maintenance. In fact,
39#
發(fā)表于 2025-3-28 08:18:31 | 只看該作者
40#
發(fā)表于 2025-3-28 14:05:14 | 只看該作者
Learning Trading Rules with Inductive Logic ProgrammingTo do this, we first identify ideal trading opportunities in the historical data, and then feed these as examples to an ILP learner, which will try to induce a description of them in terms of a given set of indicators. The main contributions of this paper are twofold. Conceptually, we are learning s
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