💡 Words with a Similar Meaning to "Sample complexity"
Found via reverse dictionary — words that share a conceptual meaning.
| Word | Definition |
|---|---|
| space complexitynoun | (computer science) A measure of the amount of space, or memory required by an algorithm to solve a given decision problem. An informal analogy would be the amount of scratch paper needed while working out a problem with pen and paper. |
| supervised learning | In machine learning, supervised learning is a paradigm where a model is trained using input objects (e.g. a vector of predictor variables) and desired output values (also known as a supervisory signal), which are often human-made labels. |
| feature learning | In machine learning, feature learning or representation learning is a set of techniques that allow a system to automatically discover the representations needed for feature detection or classification from raw data. |
| time complexitynoun | (computer science) The amount of time an algorithm requires to run, as a function of the amount of input, measured in such a way as to ignore constant terms and multiplication by constant terms. |
| intrinsic dimension | The intrinsic dimension for a data set can be thought of as the number of variables needed in a minimal representation of the data. |
| learning rate | In machine learning and statistics, the learning rate is a tuning parameter in an optimization algorithm that determines the step size at each iteration while moving toward a minimum of a loss function. |
| nested sampling algorithm | The nested sampling algorithm is a computational approach to the Bayesian statistics problems of comparing models and generating samples from posterior distributions. |
| sequence labeling | In machine learning, sequence labeling is a type of pattern recognition task that involves the algorithmic assignment of a categorical label to each member of a sequence of observed values. |
| hyperparameternoun | (machine learning) A parameter whose value is set before the learning process begins. |
| multiclass classification | In machine learning and statistical classification, multiclass classification or multinomial classification is the problem of classifying instances into one of three or more classes (classifying instances into one of two classes is called binary classification). |
| probabilistic neural network | A probabilistic neural network is a feedforward neural network, which is widely used in classification and pattern recognition problems. |
| featurenoun | An important or main item. |
| marginnoun | (finance) The yield or profit; the selling price minus the cost of production. |
| probabilistic classification | In machine learning, a probabilistic classifier is a classifier that is able to predict, given an observation of an input, a probability distribution over a set of classes, rather than only outputting the most likely class that the observation should belong to. |
| linear classifier | In machine learning, a linear classifier makes a classification decision for each object based on a linear combination of its features. |
| ensemble learning | In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. |
| semi-supervised learning | — |
| multi-label classification | In machine learning, multi-label classification or multi-output classification is a variant of the classification problem where multiple nonexclusive labels may be assigned to each instance. |
| activation functionnoun | (computer science) A function that defines the output of a particular node in an artificial neural network on the basis of its inputs. |
| manifold regularization | In machine learning, Manifold regularization is a technique for using the shape of a dataset to constrain the functions that should be learned on that dataset. |
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