検索

Results of 51 - 60 of about 143 for [SIMILAR] 16 1024 4096 WITH 9121 training WITH 8... (0.134 sec.)

Trains on an entire dataset read from file, for a period of time using the Casca... 0: « fann_cascadetrain_on_data fann_clear_scaling_params » PHP Manual Fann 関数 Trains on an entire dataset read from file, for a period of time using the Cascade2 training algorithm fann_cascadetrain_on_file (PECL fann >= 1.0.0) fann_cascadetrain_on_file — Trains on an entire dataset read ...; https://man.plustar.jp/php/function.fann-cascadetrain-on-file.html - [similar]

Calculate input and output scaling parameters for future use based on training d... 0: « fann_set_sarprop_weight_decay_shift fann_set_train_error_function » PHP Manual Fann 関数 Calculate input and output scaling parameters for future use based on training data fann_set_scaling_params (PECL fann >= 1.0.0) fann_set_scaling_params — Calculate input and output scaling paramet ...; https://man.plustar.jp/php/function.fann-set-scaling-params.html - [similar]

Initialize the weights using Widrow + Nguyen’s algorithm 0: « fann_get_training_algorithm fann_length_train_data » PHP Manual Fann 関数 Initialize the weights using Widrow + Nguyen's algorithm fann_init_weights (PECL fann >= 1.0.0) fann_init_weights — Initialize the weights using Widrow + Nguyen's algorithm 説明 fann_init_weights ( resource $ann ...; https://man.plustar.jp/php/function.fann-init-weights.html - [similar]

Descale input and output data based on previously calculated parameters 0: « fann_descale_output fann_destroy_train » PHP Manual Fann 関数 Descale input and output data based on previously calculated parameters fann_descale_train (PECL fann >= 1.0.0) fann_descale_train — Descale input and output data based on previously calculated parameters 説明 fann_descale_t ...; https://man.plustar.jp/php/function.fann-descale-train.html - [similar]

Scale input and output data based on previously calculated parameters 0: « fann_scale_train_data fann_set_activation_function_hidden » PHP Manual Fann 関数 Scale input and output data based on previously calculated parameters fann_scale_train (PECL fann >= 1.0.0) fann_scale_train — Scale input and output data based on previously calculated parameters 説明 fan ...; https://man.plustar.jp/php/function.fann-scale-train.html - [similar]

Calculate input scaling parameters for future use based on training data 0: « fann_set_error_log fann_set_learning_momentum » PHP Manual Fann 関数 Calculate input scaling parameters for future use based on training data fann_set_input_scaling_params (PECL fann >= 1.0.0) fann_set_input_scaling_params — Calculate input scaling parameters for future use based on tr ...; https://man.plustar.jp/php/function.fann-set-input-scaling-params.html - [similar]

Calculate output scaling parameters for future use based on training data 0: « fann_set_learning_rate fann_set_quickprop_decay » PHP Manual Fann 関数 Calculate output scaling parameters for future use based on training data fann_set_output_scaling_params (PECL fann >= 1.0.0) fann_set_output_scaling_params — Calculate output scaling parameters for future use based ...; https://man.plustar.jp/php/function.fann-set-output-scaling-params.html - [similar]

The SVM class 0: « 例 SVM::__construct » PHP Manual SVM The SVM class The SVM class (PECL svm >= 0.1.0) はじめにクラス概要 class SVM { /* Constants */ const int C_SVC = 0 ; const int NU_SVC = 1 ; const int ONE_CLASS = 2 ; const int EPSILON_SVR = 3 ; const int NU_SVR = 4 ; const int KERNEL_LINEAR = 0 ; c ...; https://man.plustar.jp/php/class.svm.html - [similar]

Reads the mean square error from the network 0: « fann_get_learning_rate fann_get_network_type » PHP Manual Fann 関数 Reads the mean square error from the network fann_get_MSE (PECL fann >= 1.0.0) fann_get_MSE — Reads the mean square error from the network 説明 fann_get_MSE ( resource $ann ): float Reads the mean square error from the ...; https://man.plustar.jp/php/function.fann-get-mse.html - [similar]

例 0: « リソース型 SVM » PHP Manual SVM 例例 The basic process is to define parameters, supply training data to generate a model on, then make predictions based on the model. There are a default set of parameters that should get some results with most any input, so we'll start by looking at t ...; https://man.plustar.jp/php/svm.examples.html - [similar]