یادگیری ماشین

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انواع مساله‌ها در یادگیری ماشین

در مثال قبل، ورودی برای سادگی کار یک بعدی در نظر گرفته شد ولی این ورودی به طور معمول، به صورت یک بردار بعدی به شکل قابل نمایش است. به مجموعه‌ی این ابعاد که توصیف‌گر خروجی هستند، مجموعه‌ی ويژگیهای مساله گفته می‌شود که می‌تواند بسته به مساله بسیار بزرگ (چندین هزار بعد) و یا نسبتا کوچک باشد.

فرض کنید که یک تابع فرضیه به شکل

\begin{equation} h: D \rightarrow R \end{equation}

داریم که و به ترتیب دامنه و برد این تابع هستند (برای سادگی کار دامنه‌ی آزمون فرضیه یک‌بعدی در نظر گرفته شده است). در یادگیری ماشین بر حسب اینکه برد تابع فرضیه‌ی مورد استفاده چه خصوصیاتی دارد می‌توان مساله‌ها را به دو دسته‌ی کلی زیر دسته‌بندی نمود:

  • مساله‌ی دسته‌بندی (classification) : که در آن داریم ، که بدین معنی است که برد تابع فرضیه دارای تعداد متناهی عضو است. در این حالت هدف این است که داده‌های مورد نظر را در تعداد محدودی کلاس قراردهیم و یا به زبانی دیگر آن‌ها را با تعدادی برچسب، برچسب‌گذاری نمائیم. برای مثال:

\begin{equation} h(x) = \begin{cases} -1 ~~\text{3x + 1 > 0} \newline +1 ~~\text{otherwise}. \end{cases} \end{equation}

  • مساله‌ی رگرسیون (regression): در این حالت تعداد حالات ممکن برای خروجی تابع فرضیه بی‌نهایت است یعنی . برای مثال:

\begin{equation} h(x) = 3x + 4 \end{equation}

  • مساله‌ی خوشه‌بندی (clustering): در این نوع از مساله‌ها ما می‌دانیم که خروجی تابع فرضیه (تعداد کلاس‌ ‌ها یا برچسب‌ها) محدود و متناهی است ولی برچسب هر ورودی را در اختیار نداریم و در واقع می‌خواهیم داده‌های ورودی را در به در تعدادی سبد خوشه‌بندی کنیم (در واقع در این حالت تعداد کلاس‌ها و یا خوشه‌ها در اغلب موارد خود یک متغیر ورودی مساله‌ است).

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