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  2. CURE algorithm - Wikipedia

    en.wikipedia.org/wiki/CURE_algorithm

    CURE (Clustering Using REpresentatives) is an efficient data clustering algorithm for large databases [citation needed]. Compared with K-means clustering it is more robust to outliers and able to identify clusters having non-spherical shapes and size variances.

  3. CURE data clustering algorithm - Wikipedia

    en.wikipedia.org/?title=CURE_data_clustering...

    Pages for logged out editors learn more. Contributions; Talk; CURE data clustering algorithm

  4. Learning curve (machine learning) - Wikipedia

    en.wikipedia.org/wiki/Learning_curve_(machine...

    Many optimization algorithms are iterative, repeating the same step (such as backpropagation) until the process converges to an optimal value. Gradient descent is one such algorithm. If θ i ∗ {\displaystyle \theta _{i}^{*}} is the approximation of the optimal θ {\displaystyle \theta } after i {\displaystyle i} steps, a learning curve is the ...

  5. Mamba (deep learning architecture) - Wikipedia

    en.wikipedia.org/wiki/Mamba_(deep_learning...

    Mamba employs a hardware-aware algorithm that exploits GPUs, by using kernel fusion, parallel scan, and recomputation. [2] The implementation avoids materializing expanded states in memory-intensive layers, thereby improving performance and memory usage. The result is significantly more efficient in processing long sequences compared to ...

  6. Quantum error correction - Wikipedia

    en.wikipedia.org/wiki/Quantum_error_correction

    In general, a quantum code for a quantum channel is a subspace , where is the state Hilbert space, such that there exists another quantum channel with () = =, where is the orthogonal projection onto .

  7. Talk:CURE algorithm - Wikipedia

    en.wikipedia.org/wiki/Talk:CURE_algorithm

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  9. Diffusion map - Wikipedia

    en.wikipedia.org/wiki/Diffusion_map

    They also explained that, when the data approximate a manifold, one can recover the geometry of this manifold by computing an approximation of the Laplace–Beltrami operator. This computation is completely insensitive to the distribution of the points and therefore provides a separation of the statistics and the geometry of the data.