We present a family of margin based online learning algorithms for various prediction tasks. In particular we derive and analyze algorithms for binary and multiclass categorization, regression, uniclass prediction and...
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We present a family of margin based online learning algorithms for various prediction tasks. In particular we derive and analyze algorithms for binary and multiclass categorization, regression, uniclass prediction and sequence prediction. The update steps of our different algorithms are all based on analytical solutions to simple constrained optimization problems. This unified view allows us to prove worst-case loss bounds for the different algorithms and for the various decision problems based on a single lemma. Our bounds on the cumulative loss of the algorithms are relative to the smallest loss that can be attained by any fixed hypothesis, and as such are applicable to both realizable and unrealizable settings. We demonstrate some of the merits of the proposed algorithms in a series of experiments with synthetic and real data sets.
The capital asset pricing model(CAPM) is to study the quantitative relationship between the expected return rate of assets and risky assets in the securities *** the development of computer network technology and the ...
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The capital asset pricing model(CAPM) is to study the quantitative relationship between the expected return rate of assets and risky assets in the securities *** the development of computer network technology and the trading market,quantitative trading has gradually become a new way of *** essence lies in the decision of trading *** paper obtains the relative price data of the subsequent short-term investment period through the time series method,then uses the pa algorithm in machine learning to update the portfolio and verify the effectiveness of the strategy in the data *** on the exchange rate change trend of the US dollar,gold,and bitcoin within five years given in the question,we constructed dynamic portfolio models for the three assets to ensure maximum ***,we use the time series model to predict the relative price data of the next period in a continuous trading ***,we implement the papassive attack algorithm by calling the Sklearn package in Python to update the asset components in real-time every day and calculate the average annual return rate in 5 *** on the above analysis,we used the ADF unit root to test the stationarity of the time ***,we also carried out a white noise test of time *** specific test indexes include:According to CAPM's theory,this product has a high yield,with an annual interest rate of 195.849%,which is entirely ***,the annual standard deviation reached 0.919164,and the maximum retractable rate exceeded 50%,indicating a sizeable overall *** this,the composite product's Calmar ratio is 3.556,Sharpe ratio is 2.104623,and its return per unit risk is still *** with other quantitative trading models,this paper combines time series,machine learning,Monte Carlo,and other algorithms to effectively solve the portfolio investment optimization problem of nonlinear timevarying correlation structure among multiple *** enriches the
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