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2014年6月20日 · Kernel minimum squared error (KMSE) is a simple and efficient learning algorithm extensively used by the machine learning community. The solution of KMSE is typically not sparse, which may be harmful for real time applications. Current proposed algorithms show hindrances when dealing with sample flow learning systems.

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The public is invited to meetings about MPR stations KLSE, KZSE, KMSE, KLNI, KLCD, KXLC, KGAC, KNGA and KNSE. These bi-annual meetings are scheduled in March and September. For further information call 507-292-8630.

2014年6月20日 · Kernel minimum squared error (KMSE) is a simple and efficient learning algorithm extensively used by the machine learning community. The solution of KMSE is typically not sparse, which may be harmful for real time applications.

2015年12月1日 · Kernel Minimum Squared Error (KMSE) has become a hot topic in machine learning and pattern recognition in the past years. However, KMSE is essentially a binary classifier and one-against-all and one-against-one strategies are usually employed to deal with multi-class problems.

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The kernel minimum squared error estimation (KMSE) model can be viewed as a general framework that includes kernel Fisher discriminant analysis (KFDA), least squares support vector machine (LS-SVM), and kernel ridge regression (KRR) as its particular cases.

2014年6月1日 · Kernel minimum squared error (KMSE) is a simple and efficient learning algorithm extensively used by the machine learning community. The solution of KMSE is typically not...