2. Radial Basis Function
• Radial Basis functions are generally used to map the non-linearly seperable classes to
linearly separable class.
• We generally increase the dimensionality of the input feature vector.
• Radial basis function (RBF) is real valued function 𝜑 whose value depends only on the
distance between the input and some fixed point 𝒄.
𝝋 𝑿 = 𝑿 − 𝒄
• Different radial functions-
1. 𝜑 𝑟 = 𝑟2 + 𝑐2 Τ
1 2 𝑀𝑢𝑙𝑡𝑖𝑞𝑢𝑎𝑑𝑟𝑎𝑡𝑖𝑐 𝑤ℎ𝑒𝑟𝑒 𝒓 ∈ 𝑹 𝑎𝑛𝑑 𝒄 𝑖𝑠 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒 𝑐𝑜𝑛𝑠𝑡𝑎𝑛𝑡
2. 𝜑 𝑟 = 𝑒
−𝑟2
2𝜎2 𝐺𝑎𝑢𝑠𝑠𝑖𝑎𝑛 𝐹𝑢𝑛𝑐𝑡𝑖𝑜𝑛
3. φ 𝑟 =
1
𝑟2+𝑐2 Τ
1 2 𝐼𝑛𝑣𝑒𝑟𝑠𝑒 𝑄𝑢𝑎𝑑𝑟𝑎𝑡𝑖𝑐
• Radial basis functions generally used to generalized mathematical function as
𝒚 𝑿 = σ𝒊=𝟏
𝑵
𝒘𝒊𝝋 𝑿 − 𝑿𝒊
𝑊ℎ𝑒𝑟𝑒 𝒚 𝑿 𝑖𝑠 𝑎𝑝𝑝𝑟𝑜𝑥𝑖𝑚𝑎𝑡𝑖𝑛𝑔 𝑓𝑢𝑛𝑐𝑡𝑖𝑜𝑛 𝑎𝑛𝑑 𝑵 𝑖𝑠 𝑛𝑜. 𝑜𝑓 𝑅𝐵𝐹 𝑒𝑎𝑐ℎ 𝑎𝑠𝑠𝑜𝑐𝑖𝑎𝑡𝑒𝑑 𝑤𝑖𝑡ℎ 𝑐𝑒𝑛𝑡𝑒𝑟 𝑋𝑖
3. Radial Basis Function Network
• Radial basis function network can be used as kernels for approximating functions and
recognizing patterns.
• Developed by Michael. J. D. Powell in 1977 and applied to Machine Learning by David
Broomhead and David Lowe in 1988.
• Radial Basis functions use a layer which maps the non-linearly separable classes to linearly
separable class.