Numerical results and comparisons to quantization-type algorithms Qknn, as an efficient algorithm to numerically solve low-dimensional control problems, are also provided.doi:10.1007/s11009-019-09767-9Achref BachouchCme HuréNicolas LangrenéHuyên Pham...
The evaluation method used in the algorithm is the wrapper method, designed to keep a degree of balance between two objectives: (i) minimize the number of selected features, (ii) maintain a high level of accuracy. We use the k-nearest-neighbor (KNN) machine learning algorithm in the ...
Fast superresolution frequency detection using MUSIC algorithm audio music frequency dtmf signal-processing dsp voip spectral-analysis audio-processing frequency-analysis frequency-estimation doa spectral-methods dtmf-detector eigen-vector-decomposition spectral-method doa-estimation eigenvalueproblems amplitude-es...
Machine learning in action (2) —— KNN algorithm 1. KNN —— k-NearestNeighbors 2. KNN algorithm works like this: We ha... 《Imbalance problems in object detection: A review》笔记 简介 论文《Imbalance problems in object detection: A review》对目标检测中的不平衡问题做了综合的叙述。该论文...
The performance of the proposed algorithm is evaluated on different datasets and compared with three state-of-the-art boosting algorithms, k-Nearest Neighbor (KNN) and Support Vector Machine (SVM). The results show that the performance of the proposed algorithm ranks first in all but one dataset...
In the experiment, iris data set is selected, and the C4.5 algorithm, KNN algorithm, Naive Bayes algorithm, Logistic, perceptron, and maximum entropy classifiers are used to compare and observe the results shown in Figure 4. There are 300 experimental data in this paper, including 240 training...
SARSA (State-Action-Reward-State-Action) algorithm Temporal difference learning Data Mining Algorithms C4.5 k-Means SVM (Support Vector Machine) Apriori EM (Expectation-Maximization) PageRank AdaBoost KNN (K-Nearest Neighbors) Naive Bayes CART (Classification and Regression Trees) Deep Learning archite...
For this prediction task, we propose a simple learning strategy based on kNN. Let p1,...,ps be the training parameters. During training, the instances I(p1),...,I(ps) are solved usingAlgorithm 1, where the initial hints are set toRELAXfor all constraints. During the solution of each ...
where acc denotes the accuracy achieved using the KNN classifier, with K set to 5. Additionally, \(\frac{d_{s}}{D}\) represents the feature selection ratio. Previous studies have shown that the algorithm performs optimally when \(\alpha\) is set to 0.99; therefore, this study adopts the...
In Ibrahim et al. [27], the Harris-Hawks optimizer was modified for feature selection and the support vector machine as an object function. The authors propose a hybrid strategy based on the Harris-Hawk optimization (HHO) algorithm to optimize the parameters of the SVM model and find the opt...