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发布于:2018-12-14 16:47:09  访问:30 次 回复:0 篇
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Probabilistic Objective Functions For Sensor Management
Gradient Descent is one of the most popular and widely used optimization algorithms. Mean Square Error (MSE) is the most commonly used regression loss function. The performance of this model is quite good—it makes only six mistakes on the entire dataset, yielding an accuracy of about 98.9% (click the following post percentage of the instances that the model classifies correctly).
The original dataset includes three species of irises represented with four attributes, and the data mining problem is to classify each instance as belonging to one of the three species based on the attributes. A model performs well if it is able to assign high probability to the correct class of samples or a mean that is close to the true value in the test dataset.
Its main drawback is how to control de size of the external archive that implies in the computational time expended in the optimization process. Although vessel lightweight and ship initial cost functions are related, the latter has a financial appeal like the former two objective functions.
Instead of relying on pre-computed co-occurrence counts, Word2Vec takes `raw` text as input and learns a word by predicting its surrounding context (in the case of the skip-gram model) or predict a word given its surrounding context (in the case of the cBoW model) using gradient descent with randomly initialized vectors.
To validate the proposed multi-objective optimization method, it will be used to solve three examples with increasing levels of complexity, namely the optimization problem of three quadratic functions, the design of a cantilever beam and the conceptual design of a bulk carrier.
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