5. Googleでのランクづけ ● Googleが重視している項目 ○ 200個のパラメータでランクを決めている? ○ 2009年当時 ○ http://www.searchenginejournal.com/200-parameters-in-google- algorithm/15457/#ixzz1Io4iBrJI 6. Googleでのランクづけ 例 ● Visitorプロファイル ○ Visitor数 ● ドメイン関係 ○ Visitor統計(性別とか) ○ ドメイン取得日 ● ペナルティ ○ ドメイン取得からの期間 ○ 過去のペナルティ ● サーバ ○ 重複コンテンツ ○ 稼働時間 ○ 過去のハッカーによる攻撃 ○ 設置場所(国とか?) ● 設計 ○ URL構造 ○ HTML構造 ● コンテンツ ○ 言語 ○ ユニーク性 ○ コンテンツ量(text vs HTML) ● 内部
In this post, we will take a tour of the most popular machine learning algorithms. It is useful to tour the main algorithms in the field to get a feeling of what methods are available. There are so many algorithms that it can feel overwhelming when algorithm names are thrown around and you are expected to just know what they are and where they fit. I want to give you two ways to think about and ca
I love the feeling of having a new way to think about the world. I especially love when there’s some vague idea that gets formalized into a concrete concept. Information theory is a prime example of this. Information theory gives us precise language for describing a lot of things. How uncertain am I? How much does knowing the answer to question A tell me about the answer to question B? How similar
Machine Learning in Parallel with Support Vector Machines, Generalized Linear Models, and Adaptive Boosting Introduction This article describes methods for machine learning using bootstrap samples and parallel processing to model very large volumes of data in short periods of time. The R programming language includes many packages for machine learning different types of data. Three of these packag
Computer Vision: Models, Learning, and Inference Simon J.D. Prince A new machine vision textbook with 600 pages, 359 colour figures, 201 exercises and 1060 associated Powerpoint slides Published by Cambridge University Press NOW AVAILABLE from Amazon and other booksellers. "Simon Prince’s wonderful book presents a principled model-based approach to computer vision that unifies disparate algorithm
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