💡 Words with a Similar Meaning to "Temporal difference learning"
Found via reverse dictionary — words that share a conceptual meaning.
| Word | Definition |
|---|---|
| q-learningnoun | (computing theory) A model-free reinforcement learning algorithm to learn a policy telling an agent what action to take under what circumstances. |
| backpropagation through time | a gradient-based technique for training certain types of recurrent neural networks, such as Elman networks. |
| approximate bayesian computation | Approximate Bayesian computation constitutes a class of computational methods rooted in Bayesian statistics that can be used to estimate the posterior distributions of model parameters. |
| reinforcement learningnoun | (machine learning) A type of explorative learning via feedback in a simulated environment. |
| bayesian structural time series | Bayesian structural time series model is a statistical technique used for feature selection, time series forecasting, nowcasting, inferring causal impact and other applications. |
| probabilistic programming | a programming paradigm in which probabilistic models are specified and inference for these models is performed automatically. |
| 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. |
| deep reinforcement learning | (deep RL) a subfield of machine learning that combines reinforcement learning and deep learning. |
| empirical risk minimization | In statistical learning theory, the principle of empirical risk minimization defines a family of learning algorithms based on evaluating performance over a known and fixed dataset. |
| 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. |
| dynamic causal modeling | a framework for specifying models, fitting them to data and comparing their evidence using Bayesian model comparison. |
| random forestnoun | (computing) A structure composed of a collection of decision trees with controlled variance |
| delta rule | In machine learning, the delta rule is a gradient descent learning rule for updating the weights of the inputs to artificial neurons in a single-layer neural network. |
| 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. |
| differential evolution | In evolutionary computation, differential evolution is a method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. |
| probabilistic neural network | A probabilistic neural network is a feedforward neural network, which is widely used in classification and pattern recognition problems. |
| conformal prediction | a machine learning framework for uncertainty quantification that produces statistically valid prediction regions (prediction intervals) for any underlying point predictor (whether statistical, machine, or deep learning) only assuming exchangeability of the data. |
| restricted boltzmann machine | A restricted Boltzmann machine (also called a restricted Sherrington–Kirkpatrick model with external field or restricted stochastic Ising–Lenz–Little model) is a generative stochastic artificial neural network that can learn a probability distribution over its set of inputs. |
| linear prediction | a mathematical operation where future values of a discrete-time signal are estimated as a linear function of previous samples. |
| statistical learning theory | a framework for machine learning drawing from the fields of statistics and functional analysis. |
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