Update README.md

Author: hamidkm9

README.md

@@ -20,7 +20,7 @@ Key Features:
 - Resource Efficient: Low memory usage, ideal for large datasets
 
 ## 🚀 New Major Release (v0.1.3)
-The `SEFR` classifier uses kernels to solve non-linear problems. Kernels work by projecting data into a different perspective, allowing a simple linear model to capture complex, curved patterns.
+The `LinearBoost` and `SEFR` classifiers use kernels to solve non-linear problems. Kernels work by projecting data into a different perspective, allowing a simple linear model to capture complex, curved patterns.
 
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@@ -52,7 +52,9 @@ A powerful and flexible kernel that can handle very complex boundaries. It works
 Inspired by neural networks, this kernel is useful for certain classification tasks that follow a sigmoid shape.
 
 * **`gamma`**: Scales the data's influence.
-* **`coef0`**: Shifts the decision boundary.## 🚀 New Major Release (v0.1.2)
+* **`coef0`**: Shifts the decision boundary.
+*
+* ## 🚀 New Major Release (v0.1.2)
 Version 0.1.2 of **LinearBoost Classifier** is released. Here are the changes:
 
 - The codebase is refactored into a new structure.