**Theory and Applications of Competitive Prediction.** / Zhdanov, Fedor.

Research output: Thesis › Doctoral Thesis

Unpublished

**Theory and Applications of Competitive Prediction.** / Zhdanov, Fedor.

Research output: Thesis › Doctoral Thesis

Zhdanov, F 2011, 'Theory and Applications of Competitive Prediction', PhD.

Zhdanov F. Theory and Applications of Competitive Prediction. 2011. 225 p.

@phdthesis{3179d927ab3a4898ba9834cae4cd9e70,

title = "Theory and Applications of Competitive Prediction",

abstract = "Predicting the future is an important purpose of machine learning research. Inonline learning, predictions are given sequentially rather than all at once. Peoplewish to make sensible decisions sequentially in many situations of everydaylife, whether month-by-month, day-by-day, or minute-by-minute.In competitive prediction, the predictions are made by a set of experts andby a learner. The quality of the predictions is measured by a loss function.The goal of the learner is to make reliable predictions under any circumstances.The learner compares his loss with the loss of the best experts from the setand ensures that his performance is not much worse.In this thesis a general methodology is described to provide algorithms withstrong performance guarantees for many prediction problems. Specific attentionis paid to the square loss function, widely used to assess the quality ofpredictions. Four types of the sets of experts are considered in this thesis: setswith finite number of free experts (which are not required to follow any strategy),sets of experts following strategies from finite-dimensional spaces, sets ofexperts following strategies from infinite-dimensional Hilbert spaces, and setsof experts following strategies from infinite-dimensional Banach spaces. Thepower of the methodology is illustrated in the derivations of various predictionalgorithms.Two core approaches are explored in this thesis: the Aggregating Algorithmand Defensive Forecasting. These approaches are close to each other in manyinteresting cases. However, Defensive Forecasting is more general and coverssome problems which cannot be solved using the Aggregating Algorithm. TheAggregating Algorithm is more specific and is often more computationallyefficient.The empirical performance and properties of new algorithms are validatedon artificial or real world data sets. Specific areas where the algorithms canbe applied are emphasized.",

author = "Fedor Zhdanov",

year = "2011",

language = "English",

}

TY - THES

T1 - Theory and Applications of Competitive Prediction

AU - Zhdanov, Fedor

PY - 2011

Y1 - 2011

N2 - Predicting the future is an important purpose of machine learning research. Inonline learning, predictions are given sequentially rather than all at once. Peoplewish to make sensible decisions sequentially in many situations of everydaylife, whether month-by-month, day-by-day, or minute-by-minute.In competitive prediction, the predictions are made by a set of experts andby a learner. The quality of the predictions is measured by a loss function.The goal of the learner is to make reliable predictions under any circumstances.The learner compares his loss with the loss of the best experts from the setand ensures that his performance is not much worse.In this thesis a general methodology is described to provide algorithms withstrong performance guarantees for many prediction problems. Specific attentionis paid to the square loss function, widely used to assess the quality ofpredictions. Four types of the sets of experts are considered in this thesis: setswith finite number of free experts (which are not required to follow any strategy),sets of experts following strategies from finite-dimensional spaces, sets ofexperts following strategies from infinite-dimensional Hilbert spaces, and setsof experts following strategies from infinite-dimensional Banach spaces. Thepower of the methodology is illustrated in the derivations of various predictionalgorithms.Two core approaches are explored in this thesis: the Aggregating Algorithmand Defensive Forecasting. These approaches are close to each other in manyinteresting cases. However, Defensive Forecasting is more general and coverssome problems which cannot be solved using the Aggregating Algorithm. TheAggregating Algorithm is more specific and is often more computationallyefficient.The empirical performance and properties of new algorithms are validatedon artificial or real world data sets. Specific areas where the algorithms canbe applied are emphasized.

AB - Predicting the future is an important purpose of machine learning research. Inonline learning, predictions are given sequentially rather than all at once. Peoplewish to make sensible decisions sequentially in many situations of everydaylife, whether month-by-month, day-by-day, or minute-by-minute.In competitive prediction, the predictions are made by a set of experts andby a learner. The quality of the predictions is measured by a loss function.The goal of the learner is to make reliable predictions under any circumstances.The learner compares his loss with the loss of the best experts from the setand ensures that his performance is not much worse.In this thesis a general methodology is described to provide algorithms withstrong performance guarantees for many prediction problems. Specific attentionis paid to the square loss function, widely used to assess the quality ofpredictions. Four types of the sets of experts are considered in this thesis: setswith finite number of free experts (which are not required to follow any strategy),sets of experts following strategies from finite-dimensional spaces, sets ofexperts following strategies from infinite-dimensional Hilbert spaces, and setsof experts following strategies from infinite-dimensional Banach spaces. Thepower of the methodology is illustrated in the derivations of various predictionalgorithms.Two core approaches are explored in this thesis: the Aggregating Algorithmand Defensive Forecasting. These approaches are close to each other in manyinteresting cases. However, Defensive Forecasting is more general and coverssome problems which cannot be solved using the Aggregating Algorithm. TheAggregating Algorithm is more specific and is often more computationallyefficient.The empirical performance and properties of new algorithms are validatedon artificial or real world data sets. Specific areas where the algorithms canbe applied are emphasized.

M3 - Doctoral Thesis

ER -