本人編寫的incremental 隨機神經元網絡算法,該算法最大的特點是可以保證approximation特性,而且速度快效果不錯,可以作為學術上的比較和分析。目前只適合benchmark的regression問題。
具體效果可參考
G.-B. Huang, L. Chen and C.-K. Siew, “Universal Approximation Using Incremental Constructive Feedforward Networks with Random Hidden Nodes”, IEEE Transactions on Neural Networks, vol. 17, no. 4, pp. 879-892, 2006.
標簽:
incremental
編寫
神經元網絡
算法
上傳時間:
2016-09-18
上傳用戶:litianchu
The "GEE! It s Simple" package illustrates Gaussian elimination with partial pivoting, which produces a factorization of P*A into the product L*U where P is a permutation matrix, and L and U are lower and upper triangular, respectively.
The functions in this package are accurate, but they are far slower than their MATLAB equivalents (x=A\b, [L,U,p]=lu(A), and so on). They are presented here merely to illustrate and educate. "Real" production code should use backslash and lu, not this package.
標簽:
illustrates
elimination
Gaussian
pivoting
上傳時間:
2016-11-09
上傳用戶:wang5829
The "GEE! It s Simple" package illustrates Gaussian elimination with partial pivoting, which produces a factorization of P*A into the product L*U where P is a permutation matrix, and L and U are lower and upper triangular, respectively.
The functions in this package are accurate, but they are far slower than their MATLAB equivalents (x=A\b, [L,U,p]=lu(A), and so on). They are presented here merely to illustrate and educate. "Real" production code should use backslash and lu, not this package.
標簽:
illustrates
elimination
Gaussian
pivoting
上傳時間:
2014-01-21
上傳用戶:lxm