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View Code? Open in Web Editor NEWStatistical learning, data geometry, manifold learning algorithms
Statistical learning, data geometry, manifold learning algorithms
For educational and entertainment purposes. The author doesn't claim any originality. Implementations of various statistial learning approaches/ bits inherited from books such as: * Wainwright, M. (2019). High-Dimensional Statistics: A Non-Asymptotic Viewpoint (Cambridge Series in Statistical and Probabilistic Mathematics). Cambridge: Cambridge University Press * Peter D. Grünwald, 2007. "The Minimum Description Length Principle," MIT Press Books, The MIT Press, edition 1, volume 1 * Richard Hartley and Andrew Zisserman. 2000. Multiple view geometry in computer vision. Cambridge University Press. * Kevin P. Murphy. 2012. Machine Learning: A Probabilistic Perspective. The MIT Press. /* * Copyright (c) 2020-2021 Muriz Serifovic * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL * THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN * THE SOFTWARE.
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