Könyv Cost-sensitive, Scalable and Adaptive Learning Using Ensemble Methods Wei Fan

Cost-sensitive, Scalable and Adaptive Learning Using Ensemble Methods

Theory and Application

Szerző: Wei Fan
Nyelv: Angol
Kötés: Puha kötésű
Elérhetőség: Kiadói készleten rendelésre
Küldés 17-27 napon belül
24 100 Ft
Prior work in inductive learning focused on generic§algorithms that sought to reduce complexity. Thu...

Információk a könyvről

Szerző
Nyelv
Angol
Kötés
Könyv - Puha kötésű
Kiadva
2009
oldal
188
EAN
9783639134421
Enbook ID
06822143
Súly
296
Méretek
150 x 220 x 11

Teljes leírás

Prior work in inductive learning focused on generic§algorithms that sought to reduce complexity. Thus,§simplifying assumptions were made. 1. All data§resides on a single processor, and resides§entirely in main memory;§Clearly in modern organizations today, most data§resides in a distributed§architecture with only small portions being resident§in main memory at each moment. §2. Each datum is considered equally important and uniform§costs are assumed. In real world contexts, different§exemplars frequently have§varying costs.§3. All features are freely acquired with no§computational or monetary§costs. This is unrealistic for many applications,§such as medical diagnosis. Usually, the test for each§feature consumes different costs and cannot be§ignored, i.e., accurate models that only take§advantage of the most expensive features are not§acceptable. §4. Model is computed on the basis of complete§knowledge. A learned hypothesis will§be applied to scenarios that are completely§represented in the training set. This§assumption is more often violated than satisfied.§There are usually new and unknown§patterns that traditional hypotheses will either§ignore or misclassify.

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