KNN notation change

Wrong notation in KNN is changed to k-NN

Signed-off-by: brainer <jeansanghyun@gmail.com>

diff --git a/README.md b/README.md
index 7032d1d..9134e19 100644 (file)
--- a/README.md
+++ b/README.md
@@ -11,7 +11,7 @@ NNtrainer is Software Framework for Training Neural Network Models on Devices.
 
 NNtrainer is an Open Source Project. The aim of the NNtrainer is to develop Software Framework to train neural network model on embedded devices which has relatively limited resources. Rather than training the whole layers, NNtrainer trains only one or a few layers added after the feature extractor.
 
-Even though it trains part of the neural network models, NNtrainer requires quite a lot of functionalities to train from common neural network frameworks. By implementing them, it is good enough to run several examples which can help to understand how it works. There are KNN, Neural Network, Logistic Regression and Reinforcement Learning with CartPole in Applications directory and some of them use Mobilenet V2 with tensorflow-lite as a feature extractor. All of them tested on Galaxy S8 with Android and PC (Ubuntu 16.04).
+Even though it trains part of the neural network models, NNtrainer requires quite a lot of functionalities to train from common neural network frameworks. By implementing them, it is good enough to run several examples which can help to understand how it works. There are k-NN, Neural Network, Logistic Regression and Reinforcement Learning with CartPole in Applications directory and some of them use Mobilenet V2 with tensorflow-lite as a feature extractor. All of them tested on Galaxy S8 with Android and PC (Ubuntu 16.04).
 
 ## Maintainer
 * [Jijoong Moon](https://github.com/jijoongmoon)
@@ -36,7 +36,7 @@ Even though it trains part of the neural network models, NNtrainer requires quit
 This is the component which controls neural network layers. Read the configuration file ([Iniparser](https://github.com/ndevilla/iniparser) is used to parse the configuration file.) and constructs Layers including Input and Output Layer, according to configured information by the user.
 The most important role of this component is to activate forward / backward propagation. It activates inferencing and training of each layer while handling the data properly among them. There are properties to describe network model as below:
 
-- **_Type:_** Network Type - Regression, KNN, NeuralNetwork
+- **_Type:_** Network Type - Regression, k-NN, NeuralNetwork
 - **_Layers:_** Name of Layers of Network Model
 - **_Learning\_rate:_** Learning rate which is used for all Layers
 - **_Decay\_rate:_** Rate for Exponentially Decayed Learning Rate
@@ -178,7 +178,7 @@ $ ninja -C build install
 ```
 Then, you will find the libnntrainer.so and related .h files in /usr/local/lib and /usr/local/include directories.
 
-By default, the command ```ninja -C build`` generates the five example application binaries (Classification, KNN, LogisticRegression, ReinforcementLearning, and Training) you could try in build/Applications. For 'Training' as an example case,
+By default, the command ```ninja -C build`` generates the five example application binaries (Classification, k-NN, LogisticRegression, ReinforcementLearning, and Training) you could try in build/Applications. For 'Training' as an example case,
 ```bash
 $ ls build/Applications/Training/jni/ -1
 e189c96@@nntrainer_training@exe