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Signal state space
| Глоссарий по искусственному интеллекту |
A term used in the discussion of neural networks, a signal state space is the space defined by all possible outputs of model. if the model had one continuous bounded output, its state space would be that bounded interval. with two such outputs, the state space can form a rectangle with three a hyper-rectangle or cube, and so on.
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Discussion, английский
- A post, comments, and responses made within a conversation space, such as a message board, help board, community, newsgroup, or blog. these exchanges are usually publicly viewable. participation may require credentials, such as a windows live id.
- A type of text which seeks to discuss the issue rather than to persuade the reader / hearer of the truth of a proposition.
Continuous, английский
- Непрерывный
- Непрерывный; длительный; продолжающийся
- A продолженный, дли- тельный, непрерванный form, future, past, present contoid n контоид
- Непрерывный, неразрывный, сплошной, неразрезной (об элементах конструкции)
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Overfitting, английский
- A term used in neural networks, recursive partitioning, and other automated modeling areas. if the training data is considered to consist both signal and noise (i.e., noisy data), a modeling technique has begun to overfit when it begins capturing the "noise" instead of the "signal." this usually occurs when a model is allowed to increase its number of "parameters`` such as regression coefficients, splits in recursive partitioning schemes, or hidden units in neural networks. the effect of overfitting is to reduce the applicability of the model to other data sets, (i.e., to limit its generalizability). in the extreme, the model can only "predict" its input. the most straight-forward way to eliminate this effect is to require a very large number of observations per parameter in the model.
- A problem in data mining when random variations in data are misclassified as important patterns. overfitting often occurs when the data set is too small to represent the real world.
Schema theorem, английский
A theorem showing that a genetic algorithm gives an exponentially increasing number of reproductive trials to above average schema. since each chromosome contains a great many schema, the rate of schema processing in the population is very high. see also: machine learning.
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