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请输入英文单字，中文词皆可：

noblest    音标拼音: [n'oblɪst]
a. 高贵的,华丽的

高贵的,华丽的

Noble \No"ble\, a. [Compar. {Nobler}; superl. {Noblest}.] [F.
noble, fr. L. nobilis that can be or is known, well known,
famous, highborn, noble, fr. noscere to know. See {know}.]
1. Possessing eminence, elevation, dignity, etc.; above
whatever is low, mean, degrading, or dishonorable;
magnanimous; as, a noble nature or action; a noble heart.
[1913 Webster]

Statues, with winding ivy crowned, belong
To nobler poets for a nobler song. --Dryden.
[1913 Webster]

2. Grand; stately; magnificent; splendid; as, a noble
edifice.
[1913 Webster]

3. Of exalted rank; of or pertaining to the nobility;
distinguished from the masses by birth, station, or title;
highborn; as, noble blood; a noble personage.
[1913 Webster]

Note: Noble is used in the formation of self-explaining
compounds; as, noble-born, noble-hearted, noble-minded.
[1913 Webster]

{Noble gas} (Chem.), a gaseous element belonging to group
VIII of the periodic table of elements, not combining with
other elements under normal reaction conditions;
specifically, {helium}, {neon}, {argon}, {krypton},
{xenon}, or {radon}; also called {inert gas}.

{Noble metals} (Chem.), silver, gold, and platinum; -- so
called from their resistance to oxidation by air and to
dissolution by acids. Copper, mercury, aluminium,
palladium, rhodium, iridium, and osmium are sometimes
included.
[1913 Webster]

Syn: Honorable; worthy; dignified; elevated; exalted;
superior; sublime; great; eminent; illustrious;
renowned; stately; splendid; magnificent; grand;
magnanimous; generous; liberal; free.
[1913 Webster]


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  • Choosing eps and minpts for DBSCAN (R)? - Stack Overflow
    16 One common and popular way of managing the epsilon parameter of DBSCAN is to compute a k-distance plot of your dataset Basically, you compute the k-nearest neighbors (k-NN) for each data point to understand what is the density distribution of your data, for different k the KNN is handy because it is a non-parametric method
  • Estimating Choosing optimal Hyperparameters for DBSCAN
    Estimating Choosing optimal Hyperparameters for DBSCAN Asked 13 years, 3 months ago Modified 4 years, 2 months ago Viewed 24k times
  • python - scikit-learn DBSCAN memory usage - Stack Overflow
    5 Comments 0 There is the DBSCAN package available which implements Theoretically-Efficient and Practical Parallel DBSCAN It's lightening quick compared to scikit-learn and doesn't suffer from the memory issue
  • How to get the centroids in DBSCAN sklearn? - Stack Overflow
    So DBSCAN could also result in a "ball"-cluster in the center with a "circle"-cluster around it Both clusters would have the same "centroid" in that case, which is the reason why computing centroids for DBSCAN results can be highly misleading So take care when working with those centroids (or use a centroid-based method)
  • python - DBSCAN eps and min_samples - Stack Overflow
    3 sklearn cluster DBSCAN gives -1 for noise, which is an outlier, all the other values other than -1 is the cluster number or cluster group To see the total number of clusters you can use the command DBSCAN labels_ What is eps or Epsilon value used in DBScan? Epsilon is the local radius for expanding clusters
  • DBSCAN choice of epsilon through elbow method - Stack Overflow
    From the paper dbscan: Fast Density-Based Clustering with R (page 11) To find a suitable value for eps, we can plot the points’ kNN distances (i e , the distance of each point to its k-th nearest neighbor) in decreasing order and look for a knee in the plot The idea behind this heuristic is that points located inside of clusters will have a small k-nearest neighbor distance, because they
  • dbscan - setting limit on maximum cluster span - Stack Overflow
    DBSCAN indeed does not impose a total size constraint on the cluster The epsilon value is best interpreted as the size of the gap separating two clusters (that may at most contain minpts-1 objects) I believe, you are in fact not even looking for clustering: clustering is the task of discovering structure in data The structure can be simpler (such as k-means) or complex (such as the
  • Evaluation metric for parameter tuning for outlier detection . . .
    I'm working on implementing parameter tuning for outlier detection in time-series data using the DBSCAN algorithm To maximize the Silhouette score (as evaluation), I'm leveraging optuna for tuning
  • DBSCAN for clustering of geographic location data
    DBSCAN is meant to be used on the raw data, with a spatial index for acceleration The only tool I know with acceleration for geo distances is ELKI (Java) - scikit-learn unfortunately only supports this for a few distances like Euclidean distance (see sklearn neighbors NearestNeighbors)





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