Search results for “экспоненциальный градиент”

Active learning: the model itself chooses which objects to give the teacher for labelling. The problem setting, why active learning is faster than passive learning, learning curves; pool-based sampling, query synthesis and stream-based selective sampling, cost-sensitive methods; uncertainty sampling (least confidence, minimum margin, maximum entropy), query by committee, version space reduction, expected model change, expected error reduction; query synthesis by the Nelder–Mead method and by the variance reduction criterion; density weighting; exploratory actions, the ε-active and EG-active algorithms with the exponentiated gradient; active learning in crowdsourcing: reconciling annotators' labels, models of their reliability and of object difficulty, assigning tasks to annotators.

Learning on a data stream: the formulation of the online learning problem, the learning curve, the issues: efficient model updates, making the model more complex as the sample grows, the equivalence of online and offline, storing the sample and catastrophic forgetting; how online learning differs from incremental learning, continual learning, anytime algorithms; lazy learning and online prototype selection; the online naive Bayes classifier with exponential distributions of features; the perceptron, Passive-Aggressive and online gradient descent; the recursive least squares method and the Sherman–Morrison formula; incremental training of decision trees ID5R; online training of an ensemble by the Hedge(β) algorithm and its financial interpretation; online deep learning and Hedge BackProp; incremental learning of new classes, iCaRL.

Time series forecasting problems: the time series model, the forecasting horizon, the least squares method; econometrics as the main source of problems — trends, seasonalities, change points; examples of forecasting sales volumes and electricity prices; the linear autoregression model; a brief survey of forecasting methods; adaptive short-term forecasting methods: the exponential moving average and the smoothing parameter, the models of Holt, Theil–Wage, Winters with linear and exponential trend, adaptive autoregression; the tracking signal and a statistical test of model adequacy, the Trigg–Leach model; adaptive selection and adaptive composition of models, the LAVR method — local adaptation of weights with regularisation, the monotonicity constraint as a regulariser; experiments on the sales forecasting problem.