Optimal rates for k-NN density and mode estimation

نویسندگان

  • Sanjoy Dasgupta
  • Samory Kpotufe
چکیده

We present two related contributions of independent interest: (1) high-probability finite sample rates for k-NN density estimation, and (2) practical mode estimators – based on k-NN – which attain minimax-optimal rates under surprisingly general distributional conditions.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Uniform Convergence Rates for Kernel Density Estimation

Kernel density estimation (KDE) is a popular nonparametric density estimation method. We (1) derive finite-sample high-probability density estimation bounds for multivariate KDE under mild density assumptions which hold uniformly in x ∈ R and bandwidth matrices. We apply these results to (2) mode, (3) density level set, and (4) class probability estimation and attain optimal rates up to logarit...

متن کامل

ارائه مدلی غیرپارامتریک با استفاده از تکنیک k- نزدیک‌ترین همسایه در برآورد جرم مخصوص ظاهری خاک

Soil bulk density measurements are often required as an input parameter for models that predict soil processes. Nonparametric approaches are being used in various fields to estimate continuous variables. One type of the nonparametric lazy learning algorithms, a k-nearest neighbor (k-NN) algorithm was introduced and tested to estimate soil bulk density from other soil properties, including soil ...

متن کامل

Hybrid Adaptive Neural Network AUV controller design with Sliding Mode Robust Term

This work addresses an autonomous underwater vehicle (AUV) for applying nonlinear control which is capable of disturbance rejection via intelligent estimation of uncertainties. Adaptive radial basis function neural network (RBF NN) controller is proposed to approximate unknown nonlinear dynamics. The problem of designing an adaptive RBF NN controller was augmented with sliding mode robust term ...

متن کامل

Discrimination of Epileptic Events Using EEG Rhythm Decomposition

The use of time series decomposition into sub–bands of frequency to accomplish the oscillation modes in nonstationary signals is proposed. Specifically, EEG signals are decomposed into frequency subbands, and the most relevant of them are employed for the detection of epilepsy seizures. Since the computation of oscillation modes is carried out based on Time-Variant Autoregressive model paramete...

متن کامل

Ensemble Estimation of Distributional Functionals via $k$-Nearest Neighbors

The problem of accurate nonparametric estimation of distributional functionals (integral functionals of one or more probability distributions) has received recent interest due to their wide applicability in signal processing, information theory, machine learning, and statistics. In particular, k-nearest neighbor (nn) based methods have received a lot of attention due to their adaptive nature an...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2014